Table of Contents
- Load Packages
- Load Metabolomic and Metagenomic Data
- Main Figures and Tables
- Figure 2
- Figure 3
- Figure 4
- Figure 5
- Figure 6
- Table 2
- Table 3
- Table 4
- Table 5
- Supplemtal Figures and Tables
- Supplemental Figure 1
- Supplemental Figure 2
- Save Data Image
Load packages and markdown options
# BiocManager::install('EnhancedVolcano')
# BiocManager::install('microbiomeMarker')
pacman::p_load(
tidyverse, # Data wrangling and visualization
purrr, # Functional programming
ggrepel, # Visualization, repels labels on plots
knitr, # To change R markdown PDF options
cutpointr, # Calculate cutpoints
cowplot, # Plot ggplots together
caret, # Machine learning
umap, # UMAP
ggsci, # Color palette
phyloseq, # Manage genomic data
microbiomeMarker, # LEfSe analysis
grid, # Work with graphical objects
gridExtra, # Plot multiple ggplots
yingtools2, # Custom functions for data manipulation
ggpirate, # ggplot version of Pirate plot
stringr, # Work with character strings
magick, # Import pdf into ggplot
ComplexHeatmap, # Heatmap
circlize, # Color generator for heatmap
survival, # Survival analysis
survminer, # Visualize survival analysis
gtsummary, # Table visualization
gt, # Export gtsummary table
pROC, # ROC analysis
grDevices, # Alternative pdf (cairo_pdf) to save special characters
tableone, # Create table ones
rstatix, # Tidyverse statistics
EnhancedVolcano, # Volcano plot
bestglm, # Logistic regression
devtools, # Source functions from GitHub
install = F
)
# ggplot theme shortcuts
et <- element_text
eb <- element_blank
er <- element_rect
opts_chunk$set(tidy.opts = list(width.cutoff = 60), tidy = TRUE)
Figure 1
#### Relative Abundance Plot START ####
# Obtain proteobacteria abundances per sample
proteo <-
covid_kraken %>%
filter(Phylum == "Proteobacteria") %>%
count(patient_ID, wt = pctseqs, name = "Proteobacteria")
# Create ggplot ordered by proteobacteria abundance
gg_proteo <- covid_kraken %>%
left_join(proteo) %>%
arrange(Kingdom, Phylum, Class, Order, Family, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
group_by(patient_ID) %>%
arrange(Genus) %>%
mutate(cum.pct = cumsum(pctseqs),
y.text = (cum.pct + c(0, cum.pct[-length(cum.pct)]))/2) %>%
ungroup() %>%
dplyr::select(-cum.pct) %>%
mutate(Genus2=Genus) %>%
separate(Genus2, into=c("k","p","c","o","f","g","s"),sep="\\|") %>%
mutate(s=gsub(" sp\\.","",s),
tax.label=ifelse(pctseqs >= 0.25,as.character(gsub(" ","\n",g)),"")) %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
mutate(vital_des = as.factor(vital_des),
vital_des = factor(vital_des, levels = c("Deceased", "Alive"))) %>%
ggplot(aes(x=reorder(patient_ID, -Proteobacteria),y=pctseqs)) +
geom_bar(aes(fill=Genus),stat="identity") +
# geom_text(aes(y=1-y.text,label=tax.label),size=2,
# angle=90,
# lineheight=0.6) +
theme_bw() +
theme(legend.position = "none",
# axis.title.x = eb(),
axis.text.x=eb(),
# axis.text.x=et(angle = 90, color = "black"),
strip.text.x= et(angle=0,size=14),
strip.background = element_rect(fill = "white"),
axis.title.y = et(color = "black", size = 12),
axis.text.y = et(color = "black", size = 10)) +
scale_fill_manual(values=pal) +
scale_y_continuous(expand = expansion(mult = c(0.005,0.005)))+
ylab("Relative Abundance (%)\n") +
xlab("")+
facet_grid(.~vital_des, space = "free_x", scales = "free_x")
# Color facets
proteo_grob <- ggplot_gtable(ggplot_build(gg_proteo))
strip_both <- which(grepl('strip-', proteo_grob$layout$name))
fills <- rev(ggsci::pal_igv("default")(2))
k <- 1
for (i in strip_both) {
j <- which(grepl('rect', proteo_grob$grobs[[i]]$grobs[[1]]$childrenOrder))
l <- which(grepl('titleGrob', proteo_grob$grobs[[i]]$grobs[[1]]$childrenOrder))
proteo_grob$grobs[[i]]$grobs[[1]]$children[[j]]$gp$col <- fills[k]
proteo_grob$grobs[[i]]$grobs[[1]]$children[[l]]$children[[1]]$gp$col <- fills[k]
k <- k+1
}
pdf(paste0(paste0("./Results/20220711_Results/shotgun_relative_abundance_", format(Sys.Date(), format = "%Y%m%d"),".pdf")), height = 6, width = 12)
grid.draw(proteo_grob)
dev.off()
## quartz_off_screen
## 2
#### Relative Abundance Plot END ####
#### Alpha Diversity Plot Inverse Simpson START ####
mat_invsim <- mat
row.names(mat_invsim) <- mat_invsim$patient_ID
mat_invsim <- mat_invsim %>% select(-patient_ID)
mat_invsim_t <- mat_invsim %>% t()
alpha_invsim <- vegan::diversity(mat_invsim,index="invsimpson") %>%
as.data.frame()
colnames(alpha_invsim)[1] <- "InvSimpson"
alpha_invsim$patient_ID <- row.names(alpha_invsim)
# Obtain values for mean alpha diversity for alive and deceased
alpha_invsim %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
group_by(vital_des) %>%
summarise(mean = mean(InvSimpson))
## # A tibble: 2 × 2
## vital_des mean
## <chr> <dbl>
## 1 Alive 13.4
## 2 Deceased 13.8
# Obtain stats for alpha diversity
alpha_invsim_stats <-
alpha_invsim %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
rstatix::wilcox_test(InvSimpson~vital_des)
pirate_colors <- rev(ggsci::pal_igv("default")(2))
set.seed(456)
gg_alpha_invsim <- alpha_invsim %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
mutate(vital_des = as.factor(vital_des),
vital_des = factor(vital_des, levels = c("Deceased", "Alive"))) %>%
ggplot(., aes(x = vital_des, y = InvSimpson, colour = vital_des, fill = vital_des)) +
geom_pirate(cis_params = list(fill = "white", alpha = 0.5),
bars_params = list(alpha = 0.65),
lines_params = list(size = 0.5),
points_params = list(fill = "black", size = 3.5),
jitter_width = 0.75,
cis = TRUE,
violins = FALSE) +
annotate("text", x = 1.15, y = 39.5, label = paste0("Wilcoxon, W = ", alpha_invsim_stats$statistic, ", p = ", alpha_invsim_stats$p),
size = 2.3) +
theme_bw() +
theme(
panel.grid = eb(),
axis.title.y = et(size = 14, color = "black"),
axis.title.x = eb(),
axis.text = et(size = 12, color = "black"),
plot.margin = margin(t = 5, # Top margin
r = 5, # Right margin
b = 5, # Bottom margin
l = 5) # Left margin
# plot.margin = margin(t = 0.2, # Top margin
# r = 35, # Right margin
# b = 0.2, # Bottom margin
# l = 35) # Left margin
) +
ylab("Alpha Diversity\n(Inverse Simpson)\n") +
scale_fill_manual(values = pirate_colors) +
scale_color_manual(values = pirate_colors) +
scale_y_continuous(breaks = seq(0,40,5))
gg_alpha_invsim
pdf(paste0("./Results/20220711_Results/shotgun_pirate_alpha_diversity_invsim_vital_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 7)
gg_alpha_invsim
dev.off()
## quartz_off_screen
## 2
#### Alpha Diversity Plot Inverse Simpson END ####
#### Alpha Diversity Plot Shannon START ####
mat_shannon <- mat
row.names(mat_shannon) <- mat_shannon$patient_ID
mat_shannon <- mat_shannon %>% select(-patient_ID)
mat_shannon_t <- mat_shannon %>% t()
alpha_shannon <- vegan::diversity(mat_shannon,index="shannon") %>%
as.data.frame()
colnames(alpha_shannon)[1] <- "Shannon"
alpha_shannon$patient_ID <- row.names(alpha_shannon)
# Obtain values for mean alpha diversity for alive and deceased
alpha_shannon %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
group_by(vital_des) %>%
summarise(mean = mean(Shannon))
## # A tibble: 2 × 2
## vital_des mean
## <chr> <dbl>
## 1 Alive 3.24
## 2 Deceased 3.03
# Obtain stats for alpha diversity
alpha_shannon_stats <-
alpha_shannon %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
rstatix::wilcox_test(Shannon~vital_des)
pirate_colors <- rev(ggsci::pal_igv("default")(2))
set.seed(456)
gg_alpha_shannon <- alpha_shannon %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
mutate(vital_des = as.factor(vital_des),
vital_des = factor(vital_des, levels = c("Deceased", "Alive"))) %>%
ggplot(., aes(x = vital_des, y = Shannon, colour = vital_des, fill = vital_des)) +
geom_pirate(cis_params = list(fill = "white", alpha = 0.5),
bars_params = list(alpha = 0.65),
lines_params = list(size = 0.5),
points_params = list(fill = "black", size = 3.5),
jitter_width = 0.75,
cis = TRUE,
violins = FALSE) +
annotate("text", x = 1.15, y = 5, label = paste0("Wilcoxon, W = ", alpha_shannon_stats$statistic, ", p = ", alpha_shannon_stats$p),
size = 2.3) +
theme_bw() +
theme(
panel.grid = eb(),
axis.title.y = et(size = 14, color = "black"),
axis.title.x = eb(),
axis.text = et(size = 12, color = "black"),
plot.margin = margin(t = 5, # Top margin
r = 5, # Right margin
b = 5, # Bottom margin
l = 5) # Left margin
# plot.margin = margin(t = 0.2, # Top margin
# r = 35, # Right margin
# b = 0.2, # Bottom margin
# l = 35) # Left margin
) +
ylab("Alpha Diversity\n(Shannon)\n") +
scale_fill_manual(values = pirate_colors) +
scale_color_manual(values = pirate_colors) +
scale_y_continuous(breaks = seq(0,6,1))
gg_alpha_shannon
pdf(paste0("./Results/20220711_Results/shotgun_pirate_alpha_diversity_shannon_vital_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 7)
gg_alpha_shannon
dev.off()
## quartz_off_screen
## 2
#### Alpha Diversity Plot Shannon END ####
#### Alpha Diversity Plot Richness START ####
mat_richness <- mat
row.names(mat_richness) <- mat_richness$patient_ID
mat_richness <- mat_richness %>% select(-patient_ID)
mat_richness_t <- mat_richness %>% t()
alpha_richness <- vegan::specnumber(mat_richness) %>%
as.data.frame()
colnames(alpha_richness)[1] <- "Richness"
alpha_richness$patient_ID <- row.names(alpha_richness)
# Obtain values for mean alpha diversity for alive and deceased
alpha_richness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
group_by(vital_des) %>%
summarise(mean = mean(Richness))
## # A tibble: 2 × 2
## vital_des mean
## <chr> <dbl>
## 1 Alive 5231.
## 2 Deceased 4378.
# Obtain stats for alpha diversity
alpha_richness_stats <-
alpha_richness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
rstatix::wilcox_test(Richness~vital_des)
pirate_colors <- rev(ggsci::pal_igv("default")(2))
set.seed(456)
gg_alpha_richness <- alpha_richness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
mutate(vital_des = as.factor(vital_des),
vital_des = factor(vital_des, levels = c("Deceased", "Alive"))) %>%
ggplot(., aes(x = vital_des, y = Richness, colour = vital_des, fill = vital_des)) +
geom_pirate(cis_params = list(fill = "white", alpha = 0.5),
bars_params = list(alpha = 0.65),
lines_params = list(size = 0.5),
points_params = list(fill = "black", size = 3.5),
jitter_width = 0.75,
cis = TRUE,
violins = FALSE) +
annotate("text", x = 1.15, y = 9000, label = paste0("Wilcoxon, W = ", alpha_richness_stats$statistic, ", p = ", alpha_richness_stats$p),
size = 2.3) +
theme_bw() +
theme(
panel.grid = eb(),
axis.title.y = et(size = 14, color = "black"),
axis.title.x = eb(),
axis.text = et(size = 12, color = "black"),
plot.margin = margin(t = 5, # Top margin
r = 5, # Right margin
b = 5, # Bottom margin
l = 5) # Left margin
# plot.margin = margin(t = 0.2, # Top margin
# r = 35, # Right margin
# b = 0.2, # Bottom margin
# l = 35) # Left margin
) +
ylab("Alpha Diversity\n(Species Richness)\n") +
scale_fill_manual(values = pirate_colors) +
scale_color_manual(values = pirate_colors) +
scale_y_continuous(breaks = seq(0,10000,1000))
gg_alpha_richness
pdf(paste0("./Results/20220711_Results/shotgun_pirate_alpha_diversity_richness_vital_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 7)
gg_alpha_richness
dev.off()
## quartz_off_screen
## 2
#### Alpha Diversity Plot Richness END ####
#### Alpha Diversity Plot Evenness START ####
mat_evenness <- mat
row.names(mat_evenness) <- mat_evenness$patient_ID
mat_evenness <- mat_evenness %>% select(-patient_ID)
mat_evenness_t <- mat_evenness %>% t()
h <- vegan::diversity(mat_evenness)
s <- vegan::specnumber(mat)
alpha_evenness <- h/log(s)
alpha_evenness <- as.data.frame(alpha_evenness)
colnames(alpha_evenness)[1] <- "Evenness"
alpha_evenness$patient_ID <- row.names(alpha_evenness)
# Obtain values for mean alpha diversity for alive and deceased
alpha_evenness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
group_by(vital_des) %>%
summarise(mean = mean(Evenness))
## # A tibble: 2 × 2
## vital_des mean
## <chr> <dbl>
## 1 Alive 0.386
## 2 Deceased 0.375
# Obtain stats for alpha diversity
alpha_evenness_stats <-
alpha_evenness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
rstatix::wilcox_test(Evenness~vital_des)
pirate_colors <- rev(ggsci::pal_igv("default")(2))
set.seed(456)
gg_alpha_evenness <- alpha_evenness %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
mutate(vital_des = as.factor(vital_des),
vital_des = factor(vital_des, levels = c("Deceased", "Alive"))) %>%
ggplot(., aes(x = vital_des, y = Evenness, colour = vital_des, fill = vital_des)) +
geom_pirate(cis_params = list(fill = "white", alpha = 0.5),
bars_params = list(alpha = 0.65),
lines_params = list(size = 0.5),
points_params = list(fill = "black", size = 3.5),
jitter_width = 0.75,
cis = TRUE,
violins = FALSE) +
annotate("text", x = 1.15, y = 0.7, label = paste0("Wilcoxon, W = ", alpha_evenness_stats$statistic, ", p = ", alpha_evenness_stats$p),
size = 2.3) +
theme_bw() +
theme(
panel.grid = eb(),
axis.title.y = et(size = 14, color = "black"),
axis.title.x = eb(),
axis.text = et(size = 12, color = "black"),
plot.margin = margin(t = 5, # Top margin
r = 5, # Right margin
b = 5, # Bottom margin
l = 5) # Left margin
# plot.margin = margin(t = 0.2, # Top margin
# r = 35, # Right margin
# b = 0.2, # Bottom margin
# l = 35) # Left margin
) +
ylab("Alpha Diversity\n(Species Evenness)\n") +
scale_fill_manual(values = pirate_colors) +
scale_color_manual(values = pirate_colors) +
scale_y_continuous(breaks = seq(0,1,0.1))
gg_alpha_evenness
pdf(paste0("./Results/20220711_Results/shotgun_pirate_alpha_diversity_evenness_vital_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 7)
gg_alpha_evenness
dev.off()
## quartz_off_screen
## 2
#### Alpha Diversity Plot Observed END ####
#### Combine Alpha Diversity Plots START ####
gg_alpha_total <- cowplot::plot_grid(
gg_alpha_invsim + ylab("Inverse Simpson") + theme(axis.text.x = eb(),
axis.text.y = et(size = 8),
axis.title.y = et(size = 11)),
gg_alpha_shannon + ylab("Shannon") + theme(axis.text.x = eb(),
axis.text.y = et(size = 8),
axis.title.y = et(size = 11)),
gg_alpha_richness + ylab("Species Richness")+ theme(axis.text = et(size = 8),
axis.title.y = et(size = 11)),
gg_alpha_evenness + ylab("Species Evenness")+ theme(axis.text = et(size = 8),
axis.title.y = et(size = 11)),
align = "hv")
gg_alpha_total
#### Combine Alpha Diversity Plots END ####
#### UMAP Plot 1 START ####
umap_mat <- mat
umap_mat <- umap_mat %>%
as.data.frame() %>%
column_to_rownames(var = "patient_ID")
custom_config <- umap.defaults
custom_config$n_neighbors <- as.integer(nrow(umap_mat) * 0.1)
custom_config$metric <- 'manhattan'
set.seed(6)
umap_mat2 <- umap(umap_mat, config = custom_config)
umap_mat_plot <- umap_mat2$layout %>%
as.data.frame() %>%
mutate(patient_ID = row.names(.)) %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des, race, sex, Tobacco, Shock, ends_with("doms"))) %>%
ggplot(aes(x = V1, y = V2, fill = vital_des))+
geom_point(size = 3.25, alpha = 0.8, shape = 21, color = "black")+
stat_ellipse(inherit.aes = T, aes(color = vital_des), type = "norm", level = 0.75) +
stat_ellipse(inherit.aes = T, geom = "polygon",
aes(fill = vital_des),
color = "black",
alpha = 0.35,
type = "euclid", level = 0.25, show.legend = F) +
theme_bw() +
theme(panel.grid = eb(),
axis.title = et(color = "black", size = 14),
axis.text = et(color = "black", size = 12),
legend.title = et(color = "black", size = 14),
legend.text = et(color = "black", size = 12),
plot.margin = margin(t = 0, # Top margin
r = 50, # Right margin
b = 0, # Bottom margin
l = 60)) + # Left margin
# ggtitle(paste0("Shotgun Taxonomy: UMAP \nCOVID-19 Alive vs Deceased \n", "n = ", nrow(umap_mat), "\n", custom_config$n_neighbors, " Neighbors")) +
xlab("UMAP1") +
ylab("UMAP2") +
guides(color = guide_legend(title = "Vital Status"),
fill = guide_legend(title = "Vital Status"))+
ggsci::scale_color_igv()+
ggsci::scale_fill_igv() +
lims(x = c(-2.75, 3.6), # obtained from umap below: ggplot_build(umap_mat_plot_colors)$layout$panel_scales_x[[1]]$range$range
y = c(-2.614908, 3.500000))+ # obtained from umap below: ggplot_build(umap_mat_plot_colors)$layout$panel_scales_y[[1]]$range$range
coord_equal()
umap_mat_plot
#### UMAP Plot 1 END ####
#### UMAP Plot 2 START ####
# Chi-square test for proteobacteria domination and covid outcome
## Proteobacteria < 0.05 (0%) = 0
## Proteobacteria >= 0.05 (5%) = 2
## Covid outcome: Alive = 0
## Covid outcome: Deceased = 1
table(covid_lookup$proteo_doms, covid_lookup$vital_status)
##
## 0 1
## 0 30 16
## 2 9 16
# Run chi-square test
chisq <- chisq.test(covid_lookup$proteo_doms, covid_lookup$vital_status, correct = FALSE)
chisq
##
## Pearson's Chi-squared test
##
## data: covid_lookup$proteo_doms and covid_lookup$vital_status
## X-squared = 5.585, df = 1, p-value = 0.01811
corrplot::corrplot((100*chisq$residuals^2/chisq$statistic), is.cor = FALSE)
umap_mat_plot_colors_df <-
umap_mat2$layout %>%
as.data.frame() %>%
mutate(patient_ID = row.names(.)) %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des, proteo_pctseqs, entero_pctseqs)) %>%
select(patient_ID, V1, V2, vital_des, proteo_pctseqs, entero_pctseqs) %>%
mutate(proteo_col = case_when(proteo_pctseqs >= 0.05 ~ 0.5,
TRUE ~ 0),
entero_col = case_when(entero_pctseqs > 0.30 ~ 0.5,
TRUE ~ 0),
no_dom_col = case_when(proteo_pctseqs < 0.05 & entero_pctseqs < 0.30 ~ 1,
TRUE ~ 0)) %>%
pivot_longer(cols=c(proteo_col,entero_col,no_dom_col),names_to = "colorby",values_to = "amount") %>%
mutate(colorby = factor(colorby, levels = c("proteo_col", "entero_col", "no_dom_col"))) #%>%
umap_mat_plot_colors <-
ggplot(umap_mat_plot_colors_df)+
ggforce::geom_arc_bar(aes(
x0 = V1,
y0 = V2,
r0 = 0,
r = .15,
amount = amount,
fill = colorby,
color = colorby
), stat = "pie", n = 360, alpha = 0.4, size = 0.01)+
annotate("text", x = -0.75, y = 3.5, label = paste0("Chisq(", chisq$parameter, ")=",
round(chisq$statistic, 2), "; p=", round(chisq$p.value, 3)),
size = 4) +
stat_ellipse(data = umap_mat_plot_colors_df %>%
group_by(patient_ID) %>%
dplyr::slice_max(amount) %>%
filter(colorby == "proteo_col"), inherit.aes = F,
aes(x = V1, y = V2, color = colorby),
type = "t") +
stat_ellipse(data = umap_mat_plot_colors_df %>%
group_by(patient_ID) %>%
dplyr::slice_max(amount) %>%
filter(colorby == "proteo_col"), inherit.aes = F,
geom = "polygon",
aes(x = V1, y = V2, fill = colorby),
color = "black",
alpha = 0.35,
type = "euclid", level = 0.25, show.legend = F) +
theme_bw() +
theme(panel.grid = eb(),
axis.title = et(color = "black", size = 14),
axis.text = et(color = "black", size = 12),
legend.title = et(color = "black", size = 14),
legend.text = et(color = "black", size = 12),
plot.margin = margin(t = 0, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 0)) + # Left margin) +
# ggtitle(paste0("Shotgun Taxonomy: UMAP \nCOVID-19 Microbiota Dominations \n", "n = ", nrow(umap_mat), "\n", custom_config$n_neighbors, " Neighbors")) +
xlab("UMAP1") +
ylab("UMAP2") +
scale_fill_manual(labels = c("Proteobacteria >= 5%", "Enterococcus >= 30%", "No Dominations"),
values = c("proteo_col"="red","entero_col"="#129246","no_dom_col" = "gray"))+
scale_color_manual(labels = c("Proteobacteria >= 5%", "Enterococcus >= 30%", "No Dominations"),
values = c("proteo_col"="red","entero_col"="#129246","no_dom_col" = "gray"))+
guides(fill = guide_legend(title = "Microbiota Composition"),
color = guide_legend(title = "Microbiota Composition"))+
lims(x = c(-2.75, 3.6), # obtained from umap below: ggplot_build(umap_mat_plot_colors)$layout$panel_scales_x[[1]]$range$range
y = c(-2.614908, 3.500000))+ # obtained from umap below: ggplot_build(umap_mat_plot_colors)$layout$panel_scales_y[[1]]$range$range
coord_equal()
umap_mat_plot_colors
#### UMAP Plot 2 END ####
#### UMAP Plot 1 + Plot 2 START ####
pdf(paste0("./Results/shotgun_umap_umap_doms_vital_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 14)
cowplot::plot_grid(umap_mat_plot,
umap_mat_plot_colors,
nrow = 1, rel_widths = c(1, 1.134))
dev.off()
## quartz_off_screen
## 2
#### UMAP Plot 1 + Plot 2 END ####
#### LEfSe START ####
# Custom function for nomenclature
genus_species <- function(x) {
sstr <- str_split(x,"\\|")[[1]]
sstr <- gsub("_+$"," sp.",sstr)
return(paste0(sstr[length(sstr)-1], "|",sstr[length(sstr)]))
}
# Relative abundance dataframe to combine on LEfSe plot
shotgun_rel_abd <-
covid_kraken %>%
left_join(proteo) %>%
arrange(Kingdom, Phylum, Class, Order, Family, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
group_by(patient_ID) %>%
arrange(Genus) %>%
mutate(cum.pct = cumsum(pctseqs),
y.text = (cum.pct + c(0, cum.pct[-length(cum.pct)]))/2) %>%
ungroup() %>%
dplyr::select(-cum.pct) %>%
mutate(pctseqs_100 = pctseqs*100,
log10_rel = log(pctseqs_100, base = 10)) %>%
mutate(phylogeny = paste(Kingdom, Phylum, Class, Order, Family, Genus, Species, sep = " | "),
phylogeny = sapply(phylogeny, genus_species),
phylogeny = gsub(pattern = "\\s+\\|\\s+$", replacement = "", x = phylogeny)) %>%
filter(phylogeny != " | ") %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
dplyr::rename(feature = taxid)
# Build phyloseq object from shotgun data
# This is the shotgun taxonomy (otu table)
shotgun_otu <- covid_kraken %>%
mutate(Genus=paste(Kingdom,Phylum,Class,Order,Family,Genus,sep="|")) %>%
group_by(patient_ID) %>%
mutate(total=sum(numseqs)) %>%
group_by(patient_ID,total,Kingdom,Phylum,Class,Order,Family,Genus,Species,taxid) %>%
reshape2::dcast(patient_ID ~ taxid,value.var="numseqs",fill=0,fun.aggregate=sum) %>%
arrange(desc(patient_ID)) %>%
column_to_rownames(var = "patient_ID") %>%
t()
samp_covid <- sample_data(covid_lookup %>%
select(patient_ID, vital_des) %>%
column_to_rownames(var = "patient_ID")
)
# Shotgun_otu phyloseq data building
shotgun_otu_2 <- otu_table(shotgun_otu, taxa_are_rows = T)
taxmat <- matrix(sample(letters, nrow(shotgun_otu), replace = TRUE), nrow = nrow(shotgun_otu), ncol = 7)
rownames(taxmat) <- rownames(shotgun_otu)
colnames(taxmat) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
tax_tbl <- tax_table(taxmat)
phy_shotgun_otu_2 <- phyloseq(shotgun_otu_2, tax_tbl, samp_covid)
# Build a lookup
shotgun_lefse_lookup <- tax_table(phy_shotgun_otu_2) %>%
as.matrix() %>%
as.data.frame() %>%
rownames_to_column(var = "taxid") %>%
mutate(taxid = as.integer(taxid)) %>%
select(taxid) %>%
left_join(tax_lookup) %>%
mutate(phylogeny = paste(Kingdom, Phylum, Class, Order, Family, Genus, Species, sep = " | ")) %>%
select(taxid, phylogeny)
# Run LEfSe analysis using shotgun taxonomy
set.seed(123)
phy_shotgun_otu_lefse <- run_lefse(
ps = phy_shotgun_otu_2,
group = "vital_des",
subgroup = NULL,
taxa_rank = "none",
transform = "identity",
norm = "CPM",
kw_cutoff = 0.05,
lda_cutoff = 3,
bootstrap_n = 30,
bootstrap_fraction = 2/3,
wilcoxon_cutoff = 0.05,
multigrp_strat = FALSE,
sample_min = 5,
only_same_subgrp = FALSE,
curv = FALSE)
# phy_shotgun_otu_lefse@marker_table %>% view
# Plot LEfSe results
phy_shotgun_otu_lefse_df <- phy_shotgun_otu_lefse@marker_table
gg_lefse_rel_abd <-
phy_shotgun_otu_lefse_df %>%
as.matrix() %>%
as.data.frame() %>%
mutate(
ef_lda_sign = ifelse(enrich_group == "Alive", ef_lda, as.numeric(paste0("-",ef_lda))),
ef_lda_sign = as.numeric(ef_lda_sign),
ef_lda = as.numeric(ef_lda),
pvalue = as.numeric(pvalue),
padj = as.numeric(padj)) %>%
left_join(shotgun_lefse_lookup %>%
mutate(taxid = as.character(taxid)) %>%
dplyr::rename(feature = taxid)) %>%
mutate(phylogeny = sapply(phylogeny, genus_species)) %>%
filter(phylogeny != " | ") %>%
group_by(phylogeny) %>%
slice_min(padj, n = 1, with_ties = F) %>%
mutate(phylogeny = gsub(pattern = "\\s+\\|\\s+$", replacement = "", x = phylogeny)) %>%
full_join(shotgun_rel_abd %>%
mutate(feature = as.character(feature)) %>%
select(-phylogeny),
by = "feature")
gg_lefse <- gg_lefse_rel_abd %>%
filter(enrich_group == vital_des) %>%
group_by(enrich_group, feature) %>%
dplyr::slice(1) %>%
ungroup()
tax_melt <- yingtools2::get.otu.melt(phy_shotgun_otu_lefse, filter.zero = FALSE) %>%
dplyr::mutate(taxid = as.character(otu)) %>%
filter(taxid %in% phy_shotgun_otu_lefse_df[[1]]) %>%
left_join(gg_lefse %>%
select(feature, phylogeny, ef_lda), by = c("taxid"="feature")) %>%
filter(!is.na(ef_lda))
pdf(paste0("./Results/20220711_Results/shotgun_otu_lefse_analysis_",format(Sys.Date(), format = "%Y%m%d"), ".pdf"), height = 8, width = 14)
# Print Plot and save plot object to combine with other plots
gg_lefse_plot <-
gg_lefse %>%
ggplot(.,
aes(
x = reorder(phylogeny, -ef_lda_sign),
y = ef_lda_sign,
fill = enrich_group,
label = phylogeny
)) +
geom_col(color = "black", alpha = 0.8) +
geom_text(
nudge_y = ifelse(gg_lefse$ef_lda_sign > 0, 0.25, -0.25),
hjust = ifelse(gg_lefse$ef_lda_sign > 0, 0, 1),
size = 3.5
) +
theme_bw()+
theme(
panel.grid.minor = eb(),
panel.grid.major.y = eb(),
panel.border = eb(),
panel.grid.major.x = element_line(linetype="dotted", color = "grey75"),
axis.text.y = eb(),
axis.text.x = et(size = 12, color = "black"),
axis.title = et(size = 14, color = "black"),
axis.ticks.y = eb(),
legend.title = et(size = 14, color = "black"),
legend.text = et(size = 12, color = "black"),
plot.title = et(size = 16, color = "black")
)+
ggsci::scale_fill_lancet() +
scale_y_continuous(breaks = seq(-5,5,1),
limits = c(-8.5, 8.5))+
coord_flip() +
ylab("\nLDA Score (log10)") +
xlab("Phylogeny\n") +
labs(fill = "Vital Status")
gg_lefse_plot
dev.off()
## quartz_off_screen
## 2
#### LEfSe END ####
#### Combine Plots Together START ####
# Load legend
tax_legend <- magick::image_read_pdf("./Results/legend.v2.pdf")
gg_tax_legend <- ggdraw() + draw_image(tax_legend)
pdf(paste0("./Results/20220711_Results/Figure_1_",format(Sys.Date(), format = "%Y%m%d"), ".pdf"), height = 14, width = 14)
gridExtra::grid.arrange(
proteo_grob, # 1
gg_tax_legend, # 2
gg_alpha_total, # 3
umap_mat_plot, # 4
umap_mat_plot_colors, # 5
gg_lefse_plot, # 6
layout_matrix = rbind(c(1,1,1,1, NA, 3,3,3),
c(1,1,1,1, NA, 3,3,3),
c(1,1,1,1, NA, 3,3,3),
c(1,1,1,1, 2, 3,3,3),
c(1,1,1,1, 2, 3,3,3),
c(1,1,1,1, 2, 3,3,3),
c(1,1,1,1, 2, 3,3,3),
c(1,1,1,1, NA, 3,3,3),
c(1,1,1,1, NA, 3,3,3),
c(1,1,1,1, NA, 3,3,3),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(4,4,4,4, 5,5,5,5),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6),
c(6,6,6,6,6,6,6,6))
)
dev.off()
## quartz_off_screen
## 2
Figure 4A: Cutpoint Analysis
# Cutpoint dataframe
cutpoints_df <- metab_quant %>%
left_join(covid_lookup %>%
select(patient_ID, vital_status))
# Optimal cutpoints
# Create function to handle any errors during map function
safe_cutpointr <- possibly(.f = cutpointr, otherwise = "Error")
set.seed(123456)
cutpoints <-
cutpoints_df %>%
group_by(compound) %>%
group_map(~ safe_cutpointr(., mvalue, vital_status, compound,
method = maximize_metric,
metric = youden,
pos_class = 0,
neg_class = 1,
boot_runs = 2,
use_midpoints = TRUE,
na.rm = T),
.keep = TRUE)
cutpoints_unnest <- cutpoints %>%
map_df(as_tibble)
# Summary table
cutpoints_unnest_summary <-
cutpoints_unnest %>%
group_by(subgroup, pos_class) %>%
summarize(top_auc = max(AUC)) %>%
filter(top_auc == max(top_auc)) %>%
arrange(-top_auc)
##### ROC Plots START #####
# Plot ROC curves
cutpoints_unnest %>%
arrange(desc(AUC)) %>%
group_by(pos_class) %>%
ungroup() %>%
unnest(roc_curve) %>%
arrange(desc(AUC)) %>%
mutate(auc_label = paste0("AUC = ",round(AUC,5))) %>%
ggplot(aes(x = fpr, y = tpr))+
geom_line()+
geom_text(aes(label = auc_label, x = 0.6, y = 0.2))+
geom_text(aes(label = round(optimal_cutpoint, 3), y = 0.8, x = 0.2))+
facet_wrap(~subgroup+pos_class)
# Plot top results
cutpoints_unnest %>%
mutate(tvalue = as.numeric(str_extract(string = pos_class, pattern = "[0-9]\\.[0-9]+|[0-9]+")),
variable = gsub("\\s<=.*", "", pos_class)) %>%
separate(subgroup, c('group1', 'group2'), sep="__", remove = FALSE) %>%
select(-boot) %>%
mutate(group_ratio = if_else(!is.na(group2), paste(group1, group2, sep = " : "), group1)) %>%
arrange(desc(AUC)) %>%
group_by(pos_class) %>%
group_by(tvalue, variable, subgroup, group_ratio, pos_class) %>%
summarize(top_auc = max(AUC)) %>%
ungroup() %>%
arrange(desc(top_auc)) %>%
group_by(tvalue, pos_class) %>%
arrange(pos_class, tvalue, subgroup, group_ratio) %>%
droplevels() %>%
mutate(variable = "Predicting Outcomes: Alive vs Deceased") %>%
ggplot()+
geom_bar(aes(x = reorder(group_ratio, -top_auc), y = top_auc, fill = group_ratio),
stat = "identity", position = "dodge")+
geom_hline(yintercept = 0.9)+
geom_hline(yintercept = 0.8)+
geom_hline(yintercept = 0.7)+
shadowtext::geom_shadowtext(aes(x = group_ratio, y = top_auc/2.5, angle = 90, label = group_ratio), size = 4)+
shadowtext::geom_shadowtext(aes(x = group_ratio, y = top_auc * 1.015, label = round(top_auc, 2)))+
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
strip.text = element_text(size = 14, color = "black"),
axis.text.y = element_text(size = 12, color = "black"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 14, color = "black"),
axis.title.x = element_blank(),
legend.title = element_text(size = 14, color = "black"),
legend.text = element_text(size = 12, color = "black", hjust = 0),
legend.position = "none") +
ggsci::scale_fill_igv()+
xlab("\nMetabolite Concentration") +
ylab("AUC\n") +
scale_y_continuous(limits = c(0,1), breaks = seq(0,1,0.1))+
guides(fill = guide_legend(ncol = 1))+
labs(fill = "Metabolite Concentration")+
ggtitle("Predicting COVID-19 Outcomes: Alive vs Deceased")+
facet_grid(~variable)
##### ROC Plots END #####
# Build dataframe to use cutpoints
cutpoints_results <-
cutpoints_df %>%
left_join(
cutpoints_unnest %>%
dplyr::rename(compound = subgroup) %>%
select(compound, direction, optimal_cutpoint)
) %>%
mutate(
cutpoint_prediction = case_when(
compound == "deoxycholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "isodeoxycholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "lithocholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "desaminotyrosine" &
mvalue >= optimal_cutpoint ~ 0,
TRUE ~ 1
)
) %>%
group_by(patient_ID, compound) %>%
mutate(mmp_score = sum(cutpoint_prediction)) %>%
dplyr::slice(1) %>%
pivot_wider(
c(vital_status, patient_ID),
names_from = "compound",
values_from = "cutpoint_prediction"
) %>%
column_to_rownames(var = "patient_ID") %>%
relocate(vital_status, .after = last_col()) %>%
mutate(vital_status = as.factor(vital_status))
cutpoints_results_var_slct <-
cutpoints_df %>%
left_join(
cutpoints_unnest %>%
dplyr::rename(compound = subgroup) %>%
select(compound, direction, optimal_cutpoint)
) %>%
mutate(
cutpoint_prediction = case_when(
compound == "deoxycholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "isodeoxycholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "lithocholic acid" &
mvalue >= optimal_cutpoint ~ 0,
compound == "desaminotyrosine" &
mvalue >= optimal_cutpoint ~ 0,
TRUE ~ 1
)
) %>%
filter(compound %in% c("deoxycholic acid", "isodeoxycholic acid", "lithocholic acid", "desaminotyrosine")) %>%
group_by(patient_ID, vital_status) %>%
summarize(mmp_score = sum(cutpoint_prediction))
# ROC curve for MMP Score using training data
# par(pty = "s")
# n = c(4, 3, 5)
# b = c(TRUE, FALSE, TRUE)
pROC_obj <- roc(
cutpoints_results_var_slct$vital_status,
cutpoints_results_var_slct$mmp_score,
smoothed = TRUE,
ci = TRUE,
plot = TRUE,
auc.polygon = TRUE,
# max.auc.polygon = TRUE,
best.method = TRUE,
print.auc = TRUE,
print.auc.col = "black",
# print.thres = "all",
# print.thres.col = "darkblue",
col = "darkblue",
auc.polygon.border = "black",
auc.polygon.col = "lightblue",
print.thres.best.method = "youden"
)
coordinates <- coords(pROC_obj, "best", ret=c("threshold", "sens", "spec", "ppv", "npv"))
cairo_pdf(paste0("./Results/20220711_Results/Figure_4A_", format(Sys.Date(), format = "%Y%m%d"), ".pdf"), width = 8, height = 6)
pROC_obj <- roc(
cutpoints_results_var_slct$vital_status,
cutpoints_results_var_slct$mmp_score,
smoothed = TRUE,
ci = TRUE,
plot = TRUE,
auc.polygon = TRUE,
# max.auc.polygon = TRUE,
best.method = TRUE,
print.auc = TRUE,
print.auc.col = "black",
# print.thres = "all",
# print.thres.col = "darkblue",
col = "darkblue",
auc.polygon.border = "black",
auc.polygon.col = "lightblue",
print.thres.best.method = "youden"
)
text(paste("PPV:", round(coordinates$ppv,2)), x = 0.41, y = 0.45)
text(paste("NPV:", round(coordinates$npv,2)), x = 0.41, y = 0.41)
text(paste("Threshold:", round(coordinates$threshold,2)), x = 0.385, y = 0.37)
dev.off()
## quartz_off_screen
## 2
Figure 4B: Survival Analysis
# Create Score Groupings
covid_lookup2 <- covid_lookup %>%
full_join(cutpoints_results_var_slct %>%
select(mmp_score, patient_ID)) %>%
mutate(grouped_mmp_score = ifelse(mmp_score >= coords(pROC_obj, "best", ret=c("threshold", "sens", "spec", "ppv", "npv"))[1][[1]], "High Score", "Low Score"))
# KM Curves
set.seed(123)
surv_object <- Surv(time = covid_lookup2$SurvTime, event = covid_lookup2$vital_status)
fit1 <- survfit(surv_object~covid_lookup2$grouped_mmp_score, data = covid_lookup2)
ggs <- ggsurvplot(
fit1,
data = covid_lookup2,
size = 1,
palette = c("blue4","salmon"),
conf.int = TRUE,
pval = TRUE,
risk.table = TRUE,
risk.table.height = 0.25,
ggtheme = theme_bw(),
risk.table.y.text.col = T, # adjusts margins
risk.table.y.text = FALSE,
legend.labs = c("High MMP", "Low MMP"),
)
ggs
pdf(paste0("./Results/20220711_Results/Figure_4B_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 8, onefile = FALSE)
ggs
dev.off()
## quartz_off_screen
## 2
# Percent survival
summary(fit1, times=248)
## Call: survfit(formula = surv_object ~ covid_lookup2$grouped_mmp_score,
## data = covid_lookup2)
##
## 3 observations deleted due to missingness
## covid_lookup2$grouped_mmp_score=High Score
## time n.risk n.event survival std.err lower 95% CI
## 248.0000 1.0000 15.0000 0.1071 0.0922 0.0198
## upper 95% CI
## 0.5787
##
## covid_lookup2$grouped_mmp_score=Low Score
## time n.risk n.event survival std.err lower 95% CI
## 248.0000 7.0000 16.0000 0.6646 0.0685 0.5430
## upper 95% CI
## 0.8133
# High MMP = 10.7%
# Low MMP = 66.5%
Figure 5: Flow Chart
# Flow chart
ggstream <- covid_flow %>%
mutate(
Respiratory.Support = as.factor(Respiratory.Support),
Respiratory.Support = factor(
Respiratory.Support,
levels = c(
"Room Air",
"NIPPV",
"Low Flow",
"High Flow",
"Mechanical Ventilation",
"Tracheostomy"
)
)
) %>%
ggplot(aes(y = patient_ID)) +
geom_segment(aes(
x = startday,
xend = stopday,
yend = patient_ID,
color = Respiratory.Support
),
size = 2) +
geom_point(aes(x = los, y = patient_ID, shape = Discharge.Location), size = 3) +
geom_point(
aes(x = collect_days, y = patient_ID),
fill = "skyblue",
shape = 21,
size = 3
) +
theme_bw() +
theme(
strip.text = element_text(size = 10),
axis.text.y = eb(),
axis.ticks.y = eb(),
axis.text.x = et(color = "black", size = 12),
axis.title.x = et(color = "black", size = 14),
panel.grid.major.y = eb()
) +
facet_grid(Transition ~ ., scales = "free", space = "free") +
ylab("") +
scale_color_manual(values = c("#008a00",
"#67d667",
"#FFE099FF",
"#F76D5EFF",
"#A50021FF",
"grey85")) + #assigns color
# ggtitle("High Flow Nasal Cannula Success Versus Failure") +
coord_cartesian(xlim = c(-7, 100)) +
xlab("Days Relative to ICU admission") +
geom_vline(aes(xintercept = 0), linetype = "dotted") +
guides(shape = guide_legend("Discharge Location"),
color = guide_legend("Respiratory Support")) +
scale_x_continuous(expand = c(0, 0))
ggstream
ggsave(filename = paste0("./Results/20220711_Results/Figure_5_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), ggstream, height = 10, width = 10)
Figure 2A: CARD Analysis
card_dict <- card_dict %>%
filter(dbsource == "card") %>%
mutate(AMRGeneFamily = ifelse(AMRGeneFamily == "glycopeptide resistance gene cluster;van ligase",
"glycopeptide resistance gene cluster;vanA", AMRGeneFamily),
AMRGeneFamily = gsub(pattern = ";", replacement = "\n",
x = AMRGeneFamily))
card <- card %>%
right_join(covid_lookup %>%
select(patient_ID, vital_status))
card_tot <- card %>%
inner_join(card_dict) %>%
full_join(covid_lookup %>%
select(patient_ID, vital_status))
card_tot_0 <- card_tot %>%
group_by(patient_ID, ModelName) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_mean), names_from = "ModelName",
values_from = "rpkm") %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~any(. != 0))) %>%
pivot_longer(-c(patient_ID, rpkm_mean), names_to = "ModelName",
values_to = "rpkm") %>%
group_by(patient_ID, ModelName) %>%
slice_max(rpkm, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
drop_na(vital_des)
card_tot_0_family <- card_tot %>%
group_by(patient_ID, AMRGeneFamily) %>%
mutate(rpkm_sum = sum(rpkm)) %>%
dplyr::slice_max(rpkm_sum, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_sum), names_from = "AMRGeneFamily",
values_from = "rpkm") %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~any(. != 0))) %>%
pivot_longer(-c(patient_ID, rpkm_sum), names_to = "AMRGeneFamily",
values_to = "rpkm") %>%
group_by(patient_ID, AMRGeneFamily) %>%
slice_max(rpkm, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
drop_na(vital_des)
# Perform stats to find any significant differences
card_tot_0_stats <- card_tot_0 %>%
drop_na() %>%
mutate(vital_des = as.factor(vital_des)) %>%
group_by(ModelName) %>%
rstatix::wilcox_test(rpkm ~ vital_des, p.adjust.method = "none") %>%
rstatix::adjust_pvalue(method = "BH")
card_tot_0_mat <- card_tot %>%
group_by(patient_ID, ModelName) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID), names_from = "ModelName", values_from = "rpkm") %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~any(. != 0))) %>%
column_to_rownames(var = "patient_ID")
# Heatmap legend color
col_fun2 <- colorRamp2(breaks = c(0, 100), colors = c("white",
"#ad003a"))
ht_global_opt(legend_title_gp = gpar(fontsize = 10, fontface = "bold"),
legend_labels_gp = gpar(fontsize = 8))
# Create vital labels (Alive vs Deceased)
card_heatmap_labels <- card_tot_0_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(vital_des) %>%
mutate(vital_des = factor(vital_des, levels = c("Deceased",
"Alive")))
# Create groupings of CARD classes
card_heatmap_groups <- card_tot_0_mat %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "ModelName") %>%
left_join(card_dict %>%
select(ModelName, ResistanceMechanism)) %>%
select(ResistanceMechanism)
gg_card_heatmap <- card_tot_0_mat %>%
t() %>%
Heatmap(name = "RPKM", col = col_fun2, rect_gp = gpar(col = "black",
lwd = 0.2), column_names_gp = grid::gpar(fontsize = 8),
column_gap = unit(6, "mm"), column_split = card_heatmap_labels,
column_title_gp = gpar(fontsize = 14), column_title_rot = 0,
cluster_columns = TRUE, show_column_names = FALSE, show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 4), row_gap = unit(3.5,
"mm"), row_names_side = c("left"), row_split = card_heatmap_groups$ResistanceMechanism,
row_title_rot = 0, cluster_rows = TRUE, show_row_dend = FALSE)
gg_card_heatmap
pdf(paste0("./Results/20220711_Results/card_heatmap_total_",
format(Sys.Date(), format = "%Y%m%d"), ".pdf"), height = 60,
width = 16, onefile = F)
gg_card_heatmap
dev.off()
## quartz_off_screen
## 2
# Perform stats to find any significant differences
card_tot_0_family_stats <- card_tot_0_family %>%
drop_na() %>%
mutate(vital_des = as.factor(vital_des)) %>%
group_by(AMRGeneFamily) %>%
rstatix::wilcox_test(rpkm ~ vital_des, p.adjust.method = "none") %>%
rstatix::adjust_pvalue(method = "BH") %>%
filter(p < 0.05)
card_tot_0_family_mat <- card_tot %>%
group_by(patient_ID, AMRGeneFamily) %>%
mutate(rpkm_sum = sum(rpkm)) %>%
dplyr::slice_max(rpkm_sum, with_ties = F) %>%
ungroup() %>%
mutate(rpkm_sum = case_when(rpkm_sum == 0 ~ "0", between(rpkm_sum,
0, 10) ~ "0 - 10", between(rpkm_sum, 10, 50) ~ "10 - 50",
between(rpkm_sum, 50, 100) ~ "50 - 100", TRUE ~ "100+")) %>%
pivot_wider(c(patient_ID), names_from = "AMRGeneFamily",
values_from = "rpkm_sum") %>%
mutate_if(is.character, ~replace(., is.na(.), "0")) %>%
pivot_longer(-patient_ID, names_to = "AMRGeneFamily", values_to = "rpkm_sum") %>%
filter(AMRGeneFamily != "NA") %>%
group_by(patient_ID) %>%
mutate(rpkm_sum2 = ifelse(n_distinct(rpkm_sum) == 1, "ND",
rpkm_sum)) %>%
ungroup() %>%
mutate(rpkm_sum = ifelse(rpkm_sum2 == "ND", "ND", rpkm_sum)) %>%
right_join(card_tot_0_family_stats) %>%
pivot_wider(c(patient_ID), names_from = "AMRGeneFamily",
values_from = "rpkm_sum") %>%
arrange(patient_ID) %>%
column_to_rownames(var = "patient_ID")
# Create vital labels (Alive vs Deceased)
card_family_heatmap_labels <- card_tot_0_family_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(vital_des) %>%
mutate(vital_des = factor(vital_des, levels = c("Deceased",
"Alive")))
# Create groupings of CARD classes
card_family_heatmap_groups <- card_tot_0_family_mat %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "AMRGeneFamily") %>%
left_join(card_dict %>%
select(AMRGeneFamily, ResistanceMechanism) %>%
group_by(AMRGeneFamily) %>%
dplyr::slice(1)) %>%
select(ResistanceMechanism) %>%
mutate(ResistanceMechanism = str_to_title(ResistanceMechanism))
# Create row annotation of CARD classes
card_heatmap_signif <- card_tot_0_family_mat %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "AMRGeneFamily") %>%
left_join(card_tot_0_family_stats) %>%
select(AMRGeneFamily, p, p.adj) %>%
column_to_rownames(var = "AMRGeneFamily")
pvalue_col_fun = colorRamp2(c(0, 0.05), c("forestgreen", "grey90"))
# Create heatmap for unadjusted p-values
card_unadj <- card_heatmap_signif %>%
select(`Unadjusted p-value` = p) %>%
as.matrix() %>%
Heatmap(name = "Significance", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Unadjusted p-value",
column_title_rot = 0, show_column_names = F, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 0),
row_title_side = "right", show_row_names = FALSE, row_title_rot = 0,
row_split = card_family_heatmap_groups$ResistanceMechanism,
width = unit(0.15, "in"))
# Create heatmap for adjusted p-values
card_adj <- card_heatmap_signif %>%
select(`Adjusted p-value` = p.adj) %>%
as.matrix() %>%
Heatmap(name = "Significance2", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Adjusted p-value",
column_title_rot = 0, show_column_names = FALSE, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 8,
fontface = "bold"), row_title_side = "right", show_row_names = FALSE,
row_title_rot = 0, row_split = card_family_heatmap_groups$ResistanceMechanism,
width = unit(0.15, "in"))
gg_card_family_heatmap <- card_tot_0_family_mat %>%
t() %>%
Heatmap(name = "RPKM", col = c(ND = "gray80", `0` = "#f0f4f5",
`0 - 10` = "#edb4de", `10 - 50` = "#db8cc6", `50 - 100` = "#c96bb0",
`100+` = "#9c3380"), rect_gp = gpar(col = "black", lwd = 0.2),
column_names_gp = grid::gpar(fontsize = 4), column_gap = unit(6,
"mm"), column_split = card_family_heatmap_labels,
column_title_gp = gpar(fontsize = 14, just = "left"),
column_title_rot = 0, cluster_columns = TRUE, show_column_names = FALSE,
column_names_side = "top", show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 8,
fontface = "bold"), row_gap = unit(3.5, "mm"), row_names_side = c("left"),
row_split = card_family_heatmap_groups$ResistanceMechanism,
row_title_rot = 0, cluster_rows = TRUE, show_row_dend = FALSE,
heatmap_width = unit(12, "in"))
gg_card_family_heatmap
gg_card_family_heatmap_tot <- gg_card_family_heatmap + card_unadj +
card_adj
gg_card_family_heatmap_tot
pdf(paste0("./Results/20220711_Results/card_heatmap_family_",
format(Sys.Date(), format = "%Y%m%d"), ".pdf"), height = 8,
width = 20, onefile = F)
draw(gg_card_family_heatmap_tot, auto_adjust = F)
dev.off()
## quartz_off_screen
## 2
Figure 2C: Toxins Analysis
# Create simple meta data (patient_ID and vital status)
# dataframe
covid_lookup3 <- covid_lookup %>%
select(patient_ID, vital_status)
# Import toxin json file
df_toxins <- rjson::fromJSON(file = "./Data/ko02042.json")
df_toxins <- as.data.frame(df_toxins[2]) %>%
pivot_longer(everything()) %>%
mutate(name = sub("[0-9]{1}", "", name), name = sub("[0-9]{1}",
"", name), name = sub("(.*?)\\.$", "\\1", name))
# Import unique KO ID list for covid patients
df_ko_ids <- read.csv("./Data/ko_id.csv") %>%
mutate(ko_id = gsub("ko:", "", ko_id))
# Import all toxins list
df_ko_toxins <- read.csv("./Data/KO_toxins_14Apr.csv") %>%
dplyr::rename(KO = ko_id)
# Load in all KEGG data
emapper_toxins <- emapper %>%
full_join(covid_lookup %>%
select(patient_ID)) %>%
mutate(ko_id = gsub("ko:", "", ko_id)) %>%
filter(ko_id %in% df_ko_toxins$KO) %>%
select(patient_ID, ko_id) %>%
pivot_wider(names_from = "ko_id", values_from = "ko_id",
values_fill = 0, values_fn = function(x) 1) %>%
full_join(card_tot_0_family_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup3) %>%
distinct(patient_ID, vital_status), by = "patient_ID") %>%
replace(is.na(.), 3) %>%
mutate(vital_status = ifelse(vital_status == "1", "Deceased",
"Alive"), vital_status = factor(vital_status, levels = c("Deceased",
"Alive")))
df_kos_matched <- df_ko_toxins %>%
filter(KO %in% colnames(emapper_toxins))
df_kos_matched <- df_kos_matched[order(df_kos_matched$KO), ]
# Chi-square analysis Absence = 0 Presence = 1 Covid
# outcome: Alive = 0 Covid outcome: Deceased = 1
tab <- xtabs(value ~ vital_status + variable, data = emapper_toxins %>%
mutate(vital_status = ifelse(vital_status == "Alive", 0,
1)) %>%
select(-patient_ID) %>%
pivot_longer(!vital_status, names_to = "variable", values_to = "value"))
tab_result <- apply(tab, 2, chisq.test)
chi_df <- unlist(tab_result) %>%
as.data.frame() %>%
rownames_to_column(var = "KO") %>%
filter(grepl(pattern = "p\\.value", x = KO)) %>%
mutate(KO = gsub(pattern = "\\.p\\.value", replacement = "",
x = KO)) %>%
dplyr::rename(p = ".") %>%
rstatix::adjust_pvalue(method = "BH")
# Create heatmap for unadjusted p-values
toxins_unadj <- chi_df %>%
column_to_rownames(var = "KO") %>%
mutate(p = as.numeric(p)) %>%
select(`Unadjusted p-value` = p) %>%
as.matrix() %>%
Heatmap(name = "Significance", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Unadjusted p-value",
column_title_rot = 0, show_column_names = FALSE, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 0),
row_title_side = "right", show_row_names = FALSE, row_title_rot = 0,
width = unit(0.15, "in"))
# Create heatmap for adjusted p-values
toxins_adj <- chi_df %>%
column_to_rownames(var = "KO") %>%
select(`Adjusted p-value` = p.adj) %>%
as.matrix() %>%
Heatmap(name = "Significance2", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Adjusted p-value",
column_title_rot = 0, show_column_names = FALSE, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 0),
row_title_side = "right", show_row_names = FALSE, row_title_rot = 0,
width = unit(0.15, "in"))
# Heatmap legend color
col_fun3 <- structure(c("#f0f4f5", "#4689ab", "gray80"), names = c("0",
"1", "3"))
temp <- emapper_toxins %>%
full_join(covid_lookup %>%
select(patient_ID)) %>%
arrange(patient_ID) %>%
column_to_rownames(var = "patient_ID") %>%
select(-vital_status) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0))
temp <- t(temp)
temp <- temp[order(rownames(temp)), ]
temp <- cbind(df_kos_matched, temp)
rownames(temp) <- paste0(temp$KO, ": ", temp$description)
gg_toxin_heatmap <- Heatmap(data.matrix(temp[, 5:length(temp)]),
name = "Toxins Presence/Absence", heatmap_legend_param = list(labels = c("Absent",
"Present", "ND")), col = col_fun3, rect_gp = gpar(col = "black",
lwd = 0.2), column_names_gp = grid::gpar(fontsize = 8),
column_gap = unit(6, "mm"), column_split = emapper_toxins$vital_status,
column_title_gp = gpar(fontsize = 14), column_title_rot = 0,
cluster_columns = FALSE, show_column_names = FALSE, show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 8), row_gap = unit(3.5, "mm"),
row_names_side = c("left"), row_title_rot = 0, cluster_rows = FALSE,
show_row_dend = FALSE, heatmap_width = unit(11, "in"))
gg_toxin_heatmap
gg_toxin_heatmap_tot <- gg_toxin_heatmap + toxins_unadj + toxins_adj
gg_toxin_heatmap_tot
pdf(paste0("./Results/20220711_Results/toxins_heatmap_", format(Sys.Date(),
format = "%Y%m%d"), ".pdf"), height = 2.9, width = 16)
gg_toxin_heatmap_tot
dev.off()
## quartz_off_screen
## 2
Figure 2B: Bacteriocins Analysis
bactocin_covid <- bactocin %>%
full_join(covid_lookup %>% select(patient_ID, vital_status))
# Fill Clusters that have no mapped reads with 0s
bactocin_covid_tot_0 <-
bactocin_covid %>%
dplyr::rename(Cluster = `Most similar known cluster`) %>%
group_by(patient_ID, Cluster) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_mean), names_from = "Cluster", values_from = "rpkm") %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~ any(. != 0))) %>%
pivot_longer(-c(patient_ID, rpkm_mean), names_to = "Cluster", values_to = "rpkm") %>%
group_by(patient_ID, Cluster) %>%
slice_max(rpkm, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(covid_lookup %>% select(patient_ID, vital_des)) %>%
drop_na(vital_des)
# Perform stats to find any significant differences
bactocin_covid_tot_0_stats <-
bactocin_covid_tot_0 %>%
drop_na() %>%
mutate(vital_des = as.factor(vital_des)) %>%
group_by(Cluster) %>%
rstatix::wilcox_test(rpkm~vital_des, p.adjust.method = "none") %>%
rstatix::adjust_pvalue(method = "BH")
# Build matrix for heatmap
bactocin_covid_tot_0_mat <-
bactocin_covid_tot_0 %>%
group_by(patient_ID, Cluster) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(bactocin_covid_tot_0_stats) %>%
right_join(card_tot_0_family_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup3) %>%
distinct(patient_ID, vital_status), by="patient_ID") %>%
mutate(rpkm_mean = case_when(rpkm_mean == 0 ~ "0",
between(rpkm_mean, 0, 10) ~ "0 - 10",
between(rpkm_mean, 10, 50) ~ "10 - 50",
between(rpkm_mean, 50, 100) ~ "50 - 100",
TRUE ~ "100+"
)) %>%
pivot_wider(c(patient_ID), names_from = "Cluster", values_from = "rpkm_mean") %>%
mutate_if(is.character, ~replace(., is.na(.), "0")) %>%
select(where(~ any(. != 0))) %>%
pivot_longer(-patient_ID, names_to = "Cluster", values_to = "rpkm_mean") %>%
filter(Cluster != "NA") %>%
group_by(patient_ID) %>%
mutate(rpkm_mean2 = ifelse(n_distinct(rpkm_mean) == 1, "ND", rpkm_mean)) %>%
ungroup() %>%
mutate(rpkm_mean = ifelse(rpkm_mean2 == "ND", "ND", rpkm_mean)) %>%
right_join(bactocin_covid_tot_0_stats) %>%
pivot_wider(c(patient_ID), names_from = "Cluster", values_from = "rpkm_mean") %>%
arrange(patient_ID) %>%
column_to_rownames(var = "patient_ID")
# Create vital labels (Alive vs Deceased)
bactocin_heatmap_labels <-
bactocin_covid_tot_0_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup %>% select(patient_ID, vital_des)) %>%
select(vital_des) %>%
mutate(vital_des = factor(vital_des, levels = c("Deceased","Alive")))
# Create row annotation of bacteriocin classes
bactocin_heatmap_signif <-
bactocin_covid_tot_0_mat %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "Cluster") %>%
left_join(bactocin_covid_tot_0_stats) %>%
select(Cluster, p, p.adj) %>%
column_to_rownames(var = "Cluster")
# Create heatmap for unadjusted p-values
bactocin_unadj <-
bactocin_heatmap_signif %>%
select(`Unadjusted p-value` = p) %>%
as.matrix() %>%
Heatmap(name = "Significance",
cluster_rows = FALSE,
cluster_columns = FALSE,
col = pvalue_col_fun,
column_title = "Unadjusted p-value",
column_title_rot = 0,
show_column_names = FALSE,
column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2),
row_gap = unit(3.5, "mm"),
row_names_gp = gpar(fontsize = 8),
row_title_gp = gpar(fontsize = 8, fontface = "bold"),
row_title_side = "right",
show_row_names = FALSE,
row_title_rot = 0,
width = unit(0.15, "in")
)
# Create heatmap for adjusted p-values
bactocin_adj <-
bactocin_heatmap_signif %>%
select(`Adjusted p-value` = p.adj) %>%
as.matrix() %>%
Heatmap(name = "Significance2",
cluster_rows = FALSE,
cluster_columns = FALSE,
col = pvalue_col_fun,
column_title = "Adjusted p-value",
column_title_rot = 0,
show_column_names = FALSE,
column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2),
row_gap = unit(3.5, "mm"),
row_names_gp = gpar(fontsize = 8),
row_title_gp = gpar(fontsize = 8, fontface = "bold"),
row_title_side = "right",
show_row_names = FALSE,
row_title_rot = 0,
width = unit(0.15, "in")
)
# Plot bacteriocin heatmap
gg_bactocin_heatmap <-
bactocin_covid_tot_0_mat %>%
t() %>%
Heatmap(
name = "RPKM",
col = c("ND" = "gray80",
"0" = "#f0f4f5",
"0 - 10" = "#edb4de",
"10 - 50" = "#db8cc6",
"50 - 100" = "#c96bb0",
"100+" = "#9c3380"),
rect_gp = gpar(col = "black", lwd = 0.2),
column_names_gp = grid::gpar(fontsize = 4),
column_gap = unit(6, "mm"),
column_split = bactocin_heatmap_labels,
column_title_gp = gpar(fontsize = 14),
column_title_rot = 0,
cluster_columns = FALSE,
show_column_names = FALSE,
show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 8),
row_title_gp = gpar(fontsize = 8, fontface = "bold"),
row_title_side = "right",
row_gap = unit(3.5, "mm"),
row_names_side = c("left"),
row_title_rot = 0,
cluster_rows = FALSE,
show_row_dend = FALSE,
# row_dend_width = unit(4, "in"),
heatmap_width = unit(12, "in")
)
gg_bactocin_heatmap
gg_bactocin_heatmap_tot <- gg_bactocin_heatmap + bactocin_unadj + bactocin_adj
gg_bactocin_heatmap_tot
pdf(paste0("./Results/20220711_Results/bacteriocin_heatmap_total_", format(Sys.Date(), format = "%Y%m%d"),".pdf"), height = 6, width = 16, onefile = F)
gg_bactocin_heatmap_tot
dev.off()
## quartz_off_screen
## 2
Figure 2D: BAI Operon Analysis
#### Operon START ####
bai2 <- bai %>%
dplyr::rename(gene = desc) %>%
mutate(gene = ModelName)
dat2 <- dat %>%
dplyr::rename(gene = desc) %>%
mutate(gene = ModelName)
operon_tot <- bai2 %>%
bind_rows(lca) %>%
bind_rows(dat2) %>%
mutate(gene = case_when(gene == "Butyryl-CoA:acetate CoA-transferase" ~
"Butyryl CoA:\nacetate CoA transferase", gene == "putative butyrate:acetyl-CoA coenzyme A-transferase" ~
"putative butyrate:\nacetyl-CoA\ncoenzyme A-transferase",
gene == "Acetate CoA-transferase subunit beta" ~ "Acetate CoA-transferase\nsubunit beta",
gene == "Acetate CoA-transferase subunit alpha" ~ "Acetate CoA-transferase\nsubunit alpha",
gene == "Butyrate--acetoacetate CoA-transferase subunit A" ~
"Butyrate-acetoacetate\nCoA-transferase\nsubunit A",
gene == "Butyrate--acetoacetate CoA-transferase subunit B" ~
"Butyrate-acetoacetate\nCoA-transferase\nsubunit B",
TRUE ~ gene)) %>%
filter(gene != "NA", gene %in% c("5aR", "3bHSDH", "Butyrate kinase 2",
"HcrB") | str_detect(gene, "^BAI"))
# Stats
operon_pvals <- operon_tot %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(patient_ID, mappedReads, gene, rpkm) %>%
group_by(patient_ID, gene) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_mean), names_from = gene,
values_from = mappedReads) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
pivot_longer(-c(patient_ID, rpkm_mean), names_to = "gene",
values_to = "mappedReads") %>%
group_by(patient_ID, gene) %>%
slice_max(mappedReads, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
drop_na(vital_des) %>%
filter(gene != "NA") %>%
group_by(gene) %>%
rstatix::wilcox_test(rpkm_mean ~ vital_des, p.adjust.method = "none") %>%
rstatix::adjust_pvalue(method = "BH") %>%
select(gene, group1, group2, statistic, p.adj) %>%
pivot_longer(!c(gene, group1, group2), names_to = "variable",
values_to = "value") %>%
pivot_longer(!c(gene, variable, value), names_to = "vital_des",
values_to = "value2") %>%
select(-vital_des) %>%
dplyr::rename(vital_des = value2) %>%
group_by(gene, value) %>%
dplyr::slice(1) %>%
ungroup() %>%
left_join(operon_tot %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(patient_ID, mappedReads, gene, rpkm) %>%
group_by(patient_ID, gene) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_mean), names_from = gene,
values_from = mappedReads) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
pivot_longer(-c(patient_ID, rpkm_mean), names_to = "gene",
values_to = "mappedReads") %>%
group_by(patient_ID, gene) %>%
slice_max(mappedReads, n = 1, with_ties = F) %>%
ungroup() %>%
filter(gene != "NA") %>%
distinct(.keep_all = T) %>%
select(gene, rpkm_mean), by = "gene") %>%
mutate(gene = factor(gene, levels = c("BAIB", "BAICD", "BAIE",
"BAIF", "BAIG", "BAIH", "BAII", "BAIA1", "BAIA2", "3bHSDH",
"5aR", "Butyrate kinase 2", "HcrB"))) %>%
mutate(vital_des = "Deceased") %>%
distinct(.keep_all = T) %>%
pivot_wider(c(gene, vital_des, rpkm_mean), names_from = "variable",
values_from = "value")
# Heatmap
operon_tot_0 <- operon_tot %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(patient_ID, mappedReads, gene, rpkm) %>%
group_by(patient_ID, gene) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
pivot_wider(c(patient_ID, rpkm_mean), names_from = gene,
values_from = mappedReads) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~any(. != 0))) %>%
pivot_longer(-c(patient_ID, rpkm_mean), names_to = "gene",
values_to = "rpkm") %>%
group_by(patient_ID, gene) %>%
slice_max(rpkm, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
drop_na(vital_des)
# Perform stats to find any significant differences
operon_tot_0_stats <- operon_tot_0 %>%
drop_na() %>%
mutate(vital_des = as.factor(vital_des)) %>%
group_by(gene) %>%
rstatix::wilcox_test(rpkm ~ vital_des, p.adjust.method = "none") %>%
rstatix::adjust_pvalue(method = "BH")
operon_tot_0_mat <- operon_tot_0 %>%
group_by(patient_ID, gene) %>%
mutate(rpkm_mean = mean(rpkm)) %>%
dplyr::slice_max(rpkm_mean, n = 1, with_ties = F) %>%
ungroup() %>%
right_join(operon_tot_0_stats) %>%
mutate(rpkm_mean = case_when(rpkm_mean == 0 ~ "0", between(rpkm_mean,
0, 10) ~ "0 - 10", between(rpkm_mean, 10, 50) ~ "10 - 50",
between(rpkm_mean, 50, 100) ~ "50 - 100", TRUE ~ "100+")) %>%
pivot_wider(c(patient_ID), names_from = "gene", values_from = "rpkm_mean") %>%
drop_na(patient_ID) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
select(where(~any(. != 0))) %>%
column_to_rownames(var = "patient_ID")
pvalue_col_fun = colorRamp2(c(0, 0.05), c("forestgreen", "grey90"))
# Create row annotation of operon genes
operon_heatmap_signif <- operon_tot_0_mat %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "gene") %>%
left_join(operon_tot_0_stats) %>%
select(gene, p, p.adj) %>%
column_to_rownames(var = "gene")
# Create heatmap for unadjusted p-values
operon_unadj <- operon_heatmap_signif %>%
select(`Unadjusted p-value` = p) %>%
as.matrix() %>%
Heatmap(name = "Significance", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Unadjusted p-value",
column_title_rot = 0, show_column_names = FALSE, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 8,
fontface = "bold"), row_title_side = "right", show_row_names = FALSE,
row_title_rot = 0, width = unit(0.15, "in"))
# Create heatmap for adjusted p-values
operon_adj <- operon_heatmap_signif %>%
select(`Adjusted p-value` = p.adj) %>%
as.matrix() %>%
Heatmap(name = "Significance", cluster_rows = FALSE, cluster_columns = FALSE,
col = pvalue_col_fun, column_title = "Adjusted p-value",
column_title_rot = 0, show_column_names = FALSE, column_names_side = "top",
rect_gp = gpar(col = "black", lwd = 0.2), row_gap = unit(3.5,
"mm"), row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 8,
fontface = "bold"), row_title_side = "right", show_row_names = FALSE,
row_title_rot = 0, width = unit(0.15, "in"))
# Create vital labels (Alive vs Deceased)
operon_heatmap_labels <- operon_tot_0_mat %>%
rownames_to_column(var = "patient_ID") %>%
left_join(covid_lookup %>%
select(patient_ID, vital_des)) %>%
select(vital_des) %>%
mutate(vital_des = factor(vital_des, levels = c("Deceased",
"Alive")))
gg_operon_heatmap <- operon_tot_0_mat %>%
t() %>%
Heatmap(name = "RPKM", col = c(`0` = "#f0f4f5", `0 - 10` = "#edb4de",
`10 - 50` = "#db8cc6", `50 - 100` = "#c96bb0", `100+` = "#9c3380"),
rect_gp = gpar(col = "black", lwd = 0.2), column_names_gp = grid::gpar(fontsize = 8),
column_gap = unit(6, "mm"), column_split = operon_heatmap_labels,
column_title_gp = gpar(fontsize = 14), column_title_rot = 0,
cluster_columns = FALSE, show_column_names = FALSE, show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 8), row_title_gp = gpar(fontsize = 8,
fontface = "bold"), row_title_side = "right", row_gap = unit(3.5,
"mm"), row_names_side = c("left"), row_title_rot = 0,
cluster_rows = FALSE, show_row_dend = FALSE, heatmap_width = unit(12,
"in"))
gg_operon_heatmap
gg_operon_heatmap_tot <- gg_operon_heatmap + operon_unadj + operon_adj
gg_operon_heatmap_tot
pdf(paste0("./Results/20220711_Results/operon_", format(Sys.Date(),
format = "%Y%m%d"), ".pdf"), height = 4, width = 6, onefile = F)
draw(gg_operon_heatmap_tot, auto_adjust = F)
dev.off()
## quartz_off_screen
## 2
#### Operon END ####
Figure 3A: Volcano Plot
qual_log2fc <- metab_qual_raw %>%
drop_na() %>%
mutate(vital_status = as.factor(vital_status)) %>%
arrange(vital_status, patient_ID) %>%
group_by(compound) %>%
mutate(count_vital_status_0 = length(mvalue[vital_status ==
"0"]), count_vital_status_1 = length(mvalue[vital_status ==
"1"])) %>%
filter(count_vital_status_0 >= 2 & count_vital_status_1 >=
2) %>%
summarise(log2fc_val = log((mean(mvalue[vital_status == "0"],
na.rm = T)/mean(mvalue[vital_status == "1"], na.rm = T)),
base = 2)) # 0 = Alive, 1 = Deceased
qual_pval <- metab_qual_raw %>%
drop_na() %>%
mutate(vital_status = as.factor(vital_status)) %>%
arrange(vital_status, patient_ID) %>%
group_by(compound) %>%
mutate(count_vital_status_0 = length(mvalue[vital_status ==
"0"]), count_vital_status_1 = length(mvalue[vital_status ==
"1"])) %>%
filter(count_vital_status_0 >= 2 & count_vital_status_1 >=
2) %>%
rstatix::wilcox_test(mvalue ~ vital_status) %>%
rstatix::adjust_pvalue(method = "BH") %>%
add_significance("p.adj")
qual_tot <- left_join(qual_log2fc, qual_pval) %>%
column_to_rownames(var = "compound")
# Volcano Plot
set.seed(123456)
volcano <- EnhancedVolcano(qual_tot, lab = rownames(qual_tot),
x = "log2fc_val", y = "p", title = NULL, pCutoff = 0.05,
FCcutoff = 1, pointSize = 4, labSize = 5.25, labCol = "black",
caption = NULL, colAlpha = 0.65, col = c("gray75", paletteer::paletteer_d("palettetown::teamrocket")[3:5]),
xlim = c(-5.5, 5.5), ylim = c(0, 4), legendPosition = "right",
legendLabels = c(expression(p > 0.05 * ";" ~ Log[2] ~ FC <
"±" * 1), expression(p > 0.05 * ";" ~ Log[2] ~ FC >=
"±" * 1), expression(p <= 0.05 * ";" ~ Log[2] ~ FC <
"±" * 1), expression(p <= 0.05 * ";" ~ Log[2] ~ FC >=
"±" * 1)), legendLabSize = 14, drawConnectors = T, widthConnectors = 0,
arrowheads = F, gridlines.minor = F, gridlines.major = F) +
theme(axis.text = et(color = "black"), legend.text = et(hjust = 0),
plot.margin = unit(c(0, 4, 2, 4), "cm")) + labs(subtitle = NULL) +
annotate("segment", x = 1.05, xend = 3.5, y = 4, yend = 4,
arrow = arrow(), size = 2, color = (ggsci::pal_igv("default"))(2)[1]) +
annotate("text", x = 4.15, y = 4, label = "Alive", size = 6,
color = (ggsci::pal_igv("default"))(2)[1]) + annotate("rect",
xmin = 1, xmax = Inf, ymin = -log(0.05, base = 10), ymax = Inf,
alpha = 0.1, fill = (ggsci::pal_igv("default"))(2)[1]) +
annotate("segment", x = -1.05, xend = -3.5, y = 4, yend = 4,
arrow = arrow(), size = 2, color = (ggsci::pal_igv("default"))(2)[2]) +
annotate("text", x = -4.5, y = 4, label = "Deceased", size = 6,
color = (ggsci::pal_igv("default"))(2)[2]) + annotate("rect",
xmin = -1, xmax = -Inf, ymin = -log(0.05, base = 10), ymax = Inf,
alpha = 0.1, fill = (ggsci::pal_igv("default"))(2)[2]) +
guides(color = guide_legend(nrow = 4), shape = guide_legend(nrow = 4))
volcano
ggsave(plot = volcano, paste0("./Results/20220711_Results/volcano_plot_total_",
format(Sys.Date(), format = "%Y%m%d"), ".pdf"), width = 12,
height = 8, units = "in")
Figure 3B: Quant Metabolomics
#### Metabolites START #### Convert ug/mL for down-selected
#### bile acids
deoxycholic_mw <- 392.2927
isodeoxycholic_mw <- 392.2927
lithocholic_mw <- 376.2977
kw_metab_pvals <- metab_quant %>%
mutate(mvalue = case_when(compound == "deoxycholic acid" ~
(mvalue/deoxycholic_mw) * 1000, compound == "isodeoxycholic acid" ~
(mvalue/isodeoxycholic_mw) * 1000, compound == "lithocholic acid" ~
(mvalue/lithocholic_mw) * 1000, TRUE ~ mvalue), compound = case_when(compound ==
"isodeoxycholic acid" ~ "Isodeoxycholic Acid", compound ==
"lithocholic acid" ~ "Lithocholic Acid", compound ==
"deoxycholic acid" ~ "Deoxycholic Acid", compound ==
"desaminotyrosine" ~ "Desaminotyrosine", compound ==
"indole-3-carboxaldehyde" ~ "Indole-3-Carboxaldehyde")) %>%
filter(compound %in% c("Deoxycholic Acid", "Lithocholic Acid",
"Isodeoxycholic Acid", "Desaminotyrosine", "Indole-3-Carboxaldehyde")) %>%
group_by(compound) %>%
rstatix::wilcox_test(mvalue ~ vital_status, p.adjust.method = "none",
paired = F) %>%
ungroup() %>%
rstatix::adjust_pvalue(method = "BH") %>%
mutate(p.adj = round(p.adj, 3)) %>%
select(compound, group1, group2, statistic, p, p.adj) %>%
pivot_longer(!c(compound, group1, group2), names_to = "variable",
values_to = "value") %>%
pivot_longer(!c(compound, variable, value), names_to = "vital_status",
values_to = "value2") %>%
select(-vital_status) %>%
dplyr::rename(vital_status = value2) %>%
group_by(compound, variable) %>%
dplyr::slice(1) %>%
ungroup() %>%
left_join(metab_quant %>%
mutate(compound = case_when(compound == "isodeoxycholic acid" ~
"Isodeoxycholic Acid", compound == "lithocholic acid" ~
"Lithocholic Acid", compound == "deoxycholic acid" ~
"Deoxycholic Acid", compound == "desaminotyrosine" ~
"Desaminotyrosine", compound == "indole-3-carboxaldehyde" ~
"Indole-3-Carboxaldehyde")) %>%
filter(compound %in% c("Deoxycholic Acid", "Lithocholic Acid",
"Isodeoxycholic Acid", "Desaminotyrosine", "Indole-3-Carboxaldehyde")) %>%
group_by(compound) %>%
summarize(mvalue = quantile(mvalue, probs = 0.75)) %>%
ungroup() %>%
distinct(.keep_all = T) %>%
select(compound, mvalue), by = "compound") %>%
mutate(vital_status = "Deceased") %>%
distinct(.keep_all = T) %>%
pivot_wider(c(compound, vital_status, mvalue), names_from = "variable",
values_from = "value")
quant_vital_bile <- metab_quant %>%
mutate(mvalue = case_when(compound == "deoxycholic acid" ~
(mvalue/deoxycholic_mw) * 1000, compound == "isodeoxycholic acid" ~
(mvalue/isodeoxycholic_mw) * 1000, compound == "lithocholic acid" ~
(mvalue/lithocholic_mw) * 1000, TRUE ~ mvalue), compound = case_when(compound ==
"isodeoxycholic acid" ~ "Isodeoxycholic Acid", compound ==
"lithocholic acid" ~ "Lithocholic Acid", compound ==
"deoxycholic acid" ~ "Deoxycholic Acid")) %>%
filter(compound %in% c("Deoxycholic Acid", "Lithocholic Acid",
"Isodeoxycholic Acid")) %>%
ungroup() %>%
mutate(vital_status = ifelse(vital_status == "0", "Alive",
"Deceased")) %>%
ggplot(aes(x = vital_status, y = mvalue, fill = vital_status)) +
geom_boxplot(outlier.shape = NA) + geom_jitter(size = 1.6,
width = 0.3, shape = 21, fill = "black", alpha = 0.8) + geom_label(inherit.aes = F,
aes(y = mvalue * 5, x = 1.5, label = paste0("Wilcoxon,\n W = ",
statistic, ",\n", "p.adj = ", p.adj)), data = kw_metab_pvals %>%
filter(compound %in% c("Deoxycholic Acid", "Lithocholic Acid",
"Isodeoxycholic Acid")), size = 3.25, alpha = 0.9) +
theme_bw() + theme(panel.grid = eb(), axis.text = et(size = 12,
color = "black"), axis.title = et(size = 14, color = "black"),
strip.text.x = et(size = 12, color = "black", angle = 0),
legend.title = et(color = "black", size = 14), legend.text = et(color = "black",
size = 12)) + ggsci::scale_fill_igv() + facet_wrap(factor(compound,
levels = c("Deoxycholic Acid", "Lithocholic Acid", "Isodeoxycholic Acid")) ~
., scales = "free_y", nrow = 1) + ylab("Concentration (µM)\n") +
xlab("") + labs(fill = "Vital Status")
quant_vital_indole1 <- metab_quant %>%
mutate(compound = case_when(compound == "desaminotyrosine" ~
"Desaminotyrosine")) %>%
filter(compound %in% c("Desaminotyrosine")) %>%
ungroup() %>%
mutate(vital_status = ifelse(vital_status == "0", "Alive",
"Deceased")) %>%
ggplot(aes(x = vital_status, y = mvalue, fill = vital_status)) +
geom_boxplot(outlier.shape = NA) + geom_jitter(size = 1.6,
width = 0.3, shape = 21, fill = "black", alpha = 0.8) + geom_label(inherit.aes = F,
aes(y = mvalue * 5, x = 1.5, label = paste0("Wilcoxon,\n W = ",
statistic, ",\n", "p.adj = ", p.adj)), data = kw_metab_pvals %>%
filter(compound %in% c("Desaminotyrosine")), size = 3.25,
alpha = 0.9) + theme_bw() + theme(panel.grid = eb(), axis.text = et(size = 12,
color = "black"), axis.title = et(size = 14, color = "black"),
strip.text.x = et(size = 12, color = "black", angle = 0),
legend.title = et(color = "black", size = 14), legend.text = et(color = "black",
size = 12)) + ggsci::scale_fill_igv() + facet_wrap(factor(compound,
levels = c("Desaminotyrosine")) ~ ., scales = "free_y", ncol = 1) +
ylab("Concentration (µM) \n") + xlab("") + labs(fill = "Vital Status") +
ylim(c(0, 300))
quant_vital_indole2 <- metab_quant %>%
mutate(compound = case_when(compound == "indole-3-carboxaldehyde" ~
"Indole-3-Carboxaldehyde")) %>%
filter(compound %in% c("Indole-3-Carboxaldehyde")) %>%
ungroup() %>%
mutate(vital_status = ifelse(vital_status == "0", "Alive",
"Deceased")) %>%
ggplot(aes(x = vital_status, y = mvalue, fill = vital_status)) +
geom_boxplot(outlier.shape = NA) + geom_jitter(size = 1.6,
width = 0.3, shape = 21, fill = "black", alpha = 0.8) + geom_label(inherit.aes = F,
aes(y = mvalue * 1.2, x = 1.5, label = paste0("Wilcoxon,\n W = ",
statistic, ",\n", "p.adj = ", p.adj)), data = kw_metab_pvals %>%
filter(compound %in% c("Indole-3-Carboxaldehyde")), size = 3.25,
alpha = 0.9) + theme_bw() + theme(panel.grid = eb(), axis.text = et(size = 12,
color = "black"), axis.title = et(size = 14, color = "black"),
strip.text.x = et(size = 12, color = "black", angle = 0),
legend.title = et(color = "black", size = 14), legend.text = et(color = "black",
size = 12)) + ggsci::scale_fill_igv() + facet_wrap(factor(compound,
levels = c("Indole-3-Carboxaldehyde")) ~ ., scales = "free_y",
ncol = 1) + ylab("") + xlab("") + labs(fill = "Vital Status")
quant_vital_indole_tot <- cowplot::plot_grid(quant_vital_indole1 +
theme(legend.position = "none", axis.title.x = eb(), axis.title.y = eb(),
plot.margin = unit(c(0, 0, 0, 0), "cm")), quant_vital_indole2 +
theme(legend.position = "right", plot.margin = unit(c(0,
0, 0, 0), "cm"), axis.title.x = eb()), rel_widths = c(1,
1.5))
quant_vital_indole_tot
quant_vital_tot <- cowplot::plot_grid(quant_vital_bile + theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"), axis.title.x = eb()),
NULL, quant_vital_indole_tot + theme(axis.title.x = eb(),
plot.margin = unit(c(0, 0, 0, 0), "cm")), rel_widths = c(1.15,
-0.01, 1), nrow = 1, align = "hv", axis = "l")
quant_vital_tot
pdf(paste0("./Results/20220711_Results/covid_quant_metab_vital_",
format(Sys.Date(), format = "%Y%m%d"), ".pdf"), height = 4,
width = 12)
quant_vital_tot
dev.off()
## quartz_off_screen
## 2
#### Metabolites END ####
Table 1
# Create table 2
tab1_1 <- CreateTableOne(vars = tableone_vars, testNonNormal = "wilcox.test",
argsNonNormal = list(paired = FALSE, correct = TRUE, method = "BH"),
includeNA = FALSE, factorVars = tableone_cats, strata = "vital_status",
data = covid_lookup %>%
left_join(metab_quant %>%
pivot_wider(c(patient_ID, vital_status), names_from = "compound",
values_from = "mvalue")))
tab1_2 <- print(tab1_1, nonnormal = TRUE, formatOptions = list(big.mark = ","))
## Stratified by vital_status
## 0
## n 39
## race (%)
## Asian/Mideast Indian 1 ( 2.6)
## Black/African-American 25 (64.1)
## Hispanic 2 ( 5.1)
## More than one Race 1 ( 2.6)
## Native Hawaiian/Other Pacific Islander 1 ( 2.6)
## White 9 (23.1)
## sex = Male (%) 19 (48.7)
## age (median [IQR]) 58.97 [51.91, 69.01]
## bmi (median [IQR]) 31.51 [27.96, 38.78]
## HTN = 1 (%) 23 (59.0)
## HLD = 1 (%) 12 (30.8)
## DM = 1 (%) 13 (33.3)
## CHF = 1 (%) 3 ( 7.7)
## pAF = 1 (%) 3 ( 7.7)
## CAD = 1 (%) 5 (12.8)
## CVA = 1 (%) 5 (12.8)
## COPD = 1 (%) 3 ( 7.7)
## Asthma = 1 (%) 5 (12.8)
## ILD = 1 (%) 3 ( 7.7)
## OSA = 1 (%) 7 (17.9)
## Malignancy = 1 (%) 6 (15.4)
## CLD = 1 (%) 2 ( 5.1)
## CKD = 1 (%) 3 ( 7.7)
## ESRD = 1 (%) 0 ( 0.0)
## Tobacco = Never (%) 27 (69.2)
## sofa (median [IQR]) 5.00 [4.00, 9.00]
## Charlson (median [IQR]) 3.00 [2.00, 4.50]
## APACHE (median [IQR]) 18.00 [13.00, 22.50]
## PaO2FiO2 (median [IQR]) 95.00 [80.00, 160.00]
## Admission (%)
## ED 25 (64.1)
## Hospital Medicine 8 (20.5)
## OSH 6 (15.4)
## steroid = 1 (%) 28 (71.8)
## remd_treat = 1 (%) 28 (71.8)
## InvSimpson (median [IQR]) 10.77 [7.53, 18.05]
## proteo_doms = 2 (%) 9 (23.1)
## entero_doms = 2 (%) 3 ( 7.7)
## Betalactams = 1 (%) 8 (20.5)
## Levofloxicin = 1 (%) 1 ( 2.6)
## Vancomycin = 2 (%) 9 (23.1)
## Metronidazole = 1 (%) 3 ( 7.7)
## Macrolides = 1 (%) 8 (20.5)
## Doxycycline = 1 (%) 5 (12.8)
## Trimethoprim-Sulfamethoxazole = 1 (%) 5 (12.8)
## Aminoglycosides = 1 (%) 1 ( 2.6)
## Symptom_Days (median [IQR]) 5.00 [3.00, 7.00]
## ESR (median [IQR]) 84.50 [69.75, 115.25]
## CRP (median [IQR]) 77.50 [28.25, 155.00]
## PTT (median [IQR]) 34.70 [30.60, 62.15]
## LDH (median [IQR]) 451.00 [375.50, 540.75]
## Hemoglobin (median [IQR]) 12.80 [11.72, 14.38]
## Platelet (median [IQR]) 241.00 [169.50, 323.00]
## WBC (median [IQR]) 9.50 [6.65, 12.80]
## Neutrophil (median [IQR]) 71.00 [6.57, 82.00]
## Basophil (median [IQR]) 0.01 [0.00, 0.03]
## Eosinophil (median [IQR]) 0.00 [0.00, 0.00]
## 3-oxolithocholic acid (median [IQR]) 4.71 [0.64, 54.90]
## alloisolithocholic acid (median [IQR]) 0.44 [0.00, 2.79]
## taurocholic acid (median [IQR]) 0.55 [0.22, 2.23]
## cholic acid (median [IQR]) 16.21 [1.48, 215.59]
## deoxycholic acid (median [IQR]) 74.09 [18.61, 246.34]
## lithocholic acid (median [IQR]) 61.61 [4.30, 252.61]
## glycocholic acid (median [IQR]) 0.43 [0.18, 2.83]
## isodeoxycholic acid (median [IQR]) 7.60 [0.70, 25.09]
## acetate (median [IQR]) 3.87 [2.29, 20.44]
## butyrate (median [IQR]) 0.51 [0.20, 2.39]
## propionate (median [IQR]) 1.55 [0.47, 4.94]
## succinate (median [IQR]) 0.59 [0.26, 1.58]
## desaminotyrosine (median [IQR]) 28.10 [22.74, 46.94]
## indole-3-carboxaldehyde (median [IQR]) 20.97 [19.40, 23.99]
## Stratified by vital_status
## 1 p
## n 32
## race (%) 0.366
## Asian/Mideast Indian 0 ( 0.0)
## Black/African-American 19 (59.4)
## Hispanic 6 (18.8)
## More than one Race 0 ( 0.0)
## Native Hawaiian/Other Pacific Islander 0 ( 0.0)
## White 7 (21.9)
## sex = Male (%) 18 (60.0) 0.491
## age (median [IQR]) 66.21 [56.56, 72.66] 0.168
## bmi (median [IQR]) 30.73 [26.83, 34.08] 0.432
## HTN = 1 (%) 23 (71.9) 0.377
## HLD = 1 (%) 11 (34.4) 0.946
## DM = 1 (%) 12 (37.5) 0.908
## CHF = 1 (%) 3 ( 9.4) 1.000
## pAF = 1 (%) 1 ( 3.1) 0.754
## CAD = 1 (%) 1 ( 3.1) 0.302
## CVA = 1 (%) 2 ( 6.2) 0.600
## COPD = 1 (%) 1 ( 3.1) 0.754
## Asthma = 1 (%) 0 ( 0.0) 0.102
## ILD = 1 (%) 3 ( 9.4) 1.000
## OSA = 1 (%) 3 ( 9.4) 0.490
## Malignancy = 1 (%) 3 ( 9.4) 0.690
## CLD = 1 (%) 4 (12.5) 0.495
## CKD = 1 (%) 8 (25.0) 0.094
## ESRD = 1 (%) 2 ( 6.2) 0.388
## Tobacco = Never (%) 25 (78.1) 0.567
## sofa (median [IQR]) 9.00 [8.00, 9.00] <0.001
## Charlson (median [IQR]) 4.00 [2.00, 5.00] 0.551
## APACHE (median [IQR]) 23.00 [19.00, 30.25] 0.002
## PaO2FiO2 (median [IQR]) 91.00 [77.00, 158.50] 0.911
## Admission (%) 0.229
## ED 14 (43.8)
## Hospital Medicine 10 (31.2)
## OSH 8 (25.0)
## steroid = 1 (%) 23 (71.9) 1.000
## remd_treat = 1 (%) 22 (68.8) 0.985
## InvSimpson (median [IQR]) 10.80 [5.74, 20.70] 0.931
## proteo_doms = 2 (%) 16 (50.0) 0.035
## entero_doms = 2 (%) 3 ( 9.4) 1.000
## Betalactams = 1 (%) 5 (15.6) 0.825
## Levofloxicin = 1 (%) 0 ( 0.0) 1.000
## Vancomycin = 2 (%) 13 (40.6) 0.183
## Metronidazole = 1 (%) 4 (12.5) 0.782
## Macrolides = 1 (%) 6 (18.8) 1.000
## Doxycycline = 1 (%) 1 ( 3.1) 0.302
## Trimethoprim-Sulfamethoxazole = 1 (%) 2 ( 6.2) 0.600
## Aminoglycosides = 1 (%) 0 ( 0.0) 1.000
## Symptom_Days (median [IQR]) 5.00 [2.75, 8.00] 0.912
## ESR (median [IQR]) 116.00 [71.75, 120.00] 0.311
## CRP (median [IQR]) 119.00 [82.00, 176.75] 0.100
## PTT (median [IQR]) 37.95 [32.88, 58.70] 0.419
## LDH (median [IQR]) 611.00 [477.00, 686.50] 0.010
## Hemoglobin (median [IQR]) 10.95 [8.28, 12.80] <0.001
## Platelet (median [IQR]) 202.00 [119.50, 284.50] 0.055
## WBC (median [IQR]) 8.90 [7.18, 11.60] 0.720
## Neutrophil (median [IQR]) 10.51 [5.39, 86.00] 0.481
## Basophil (median [IQR]) 0.01 [0.00, 0.02] 0.605
## Eosinophil (median [IQR]) 0.00 [0.00, 0.00] 0.871
## 3-oxolithocholic acid (median [IQR]) 1.88 [0.06, 14.03] 0.086
## alloisolithocholic acid (median [IQR]) 0.20 [0.00, 6.18] 0.482
## taurocholic acid (median [IQR]) 0.30 [0.08, 24.06] 0.391
## cholic acid (median [IQR]) 7.92 [0.79, 88.15] 0.214
## deoxycholic acid (median [IQR]) 10.68 [0.49, 62.50] 0.003
## lithocholic acid (median [IQR]) 9.76 [0.24, 60.07] 0.008
## glycocholic acid (median [IQR]) 0.45 [0.03, 11.78] 0.511
## isodeoxycholic acid (median [IQR]) 2.89 [0.06, 10.93] 0.038
## acetate (median [IQR]) 3.13 [1.17, 11.23] 0.175
## butyrate (median [IQR]) 0.29 [0.06, 0.97] 0.286
## propionate (median [IQR]) 0.75 [0.19, 1.62] 0.169
## succinate (median [IQR]) 0.50 [0.18, 1.38] 0.431
## desaminotyrosine (median [IQR]) 22.63 [21.00, 32.30] 0.028
## indole-3-carboxaldehyde (median [IQR]) 20.36 [18.61, 22.74] 0.074
## Stratified by vital_status
## test
## n
## race (%)
## Asian/Mideast Indian
## Black/African-American
## Hispanic
## More than one Race
## Native Hawaiian/Other Pacific Islander
## White
## sex = Male (%)
## age (median [IQR]) nonnorm
## bmi (median [IQR]) nonnorm
## HTN = 1 (%)
## HLD = 1 (%)
## DM = 1 (%)
## CHF = 1 (%)
## pAF = 1 (%)
## CAD = 1 (%)
## CVA = 1 (%)
## COPD = 1 (%)
## Asthma = 1 (%)
## ILD = 1 (%)
## OSA = 1 (%)
## Malignancy = 1 (%)
## CLD = 1 (%)
## CKD = 1 (%)
## ESRD = 1 (%)
## Tobacco = Never (%)
## sofa (median [IQR]) nonnorm
## Charlson (median [IQR]) nonnorm
## APACHE (median [IQR]) nonnorm
## PaO2FiO2 (median [IQR]) nonnorm
## Admission (%)
## ED
## Hospital Medicine
## OSH
## steroid = 1 (%)
## remd_treat = 1 (%)
## InvSimpson (median [IQR]) nonnorm
## proteo_doms = 2 (%)
## entero_doms = 2 (%)
## Betalactams = 1 (%)
## Levofloxicin = 1 (%)
## Vancomycin = 2 (%)
## Metronidazole = 1 (%)
## Macrolides = 1 (%)
## Doxycycline = 1 (%)
## Trimethoprim-Sulfamethoxazole = 1 (%)
## Aminoglycosides = 1 (%)
## Symptom_Days (median [IQR]) nonnorm
## ESR (median [IQR]) nonnorm
## CRP (median [IQR]) nonnorm
## PTT (median [IQR]) nonnorm
## LDH (median [IQR]) nonnorm
## Hemoglobin (median [IQR]) nonnorm
## Platelet (median [IQR]) nonnorm
## WBC (median [IQR]) nonnorm
## Neutrophil (median [IQR]) nonnorm
## Basophil (median [IQR]) nonnorm
## Eosinophil (median [IQR]) nonnorm
## 3-oxolithocholic acid (median [IQR]) nonnorm
## alloisolithocholic acid (median [IQR]) nonnorm
## taurocholic acid (median [IQR]) nonnorm
## cholic acid (median [IQR]) nonnorm
## deoxycholic acid (median [IQR]) nonnorm
## lithocholic acid (median [IQR]) nonnorm
## glycocholic acid (median [IQR]) nonnorm
## isodeoxycholic acid (median [IQR]) nonnorm
## acetate (median [IQR]) nonnorm
## butyrate (median [IQR]) nonnorm
## propionate (median [IQR]) nonnorm
## succinate (median [IQR]) nonnorm
## desaminotyrosine (median [IQR]) nonnorm
## indole-3-carboxaldehyde (median [IQR]) nonnorm
# kableExtra::kable(tab1_2) %>% kableExtra::kable_classic()
# %>% kableExtra::save_kable(file = 'table_1_image.png',
# zoom = 5)
write.csv(tab1_2, paste0("./Results/20220711_Results/Table_1_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
# Need to adjust pvalues
tab1_2_padjust <- read.csv(paste0("./Results/20220711_Results/Table_1_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"))
tab1_2_padjust <- tab1_2_padjust %>%
mutate(test = ifelse(!is.na(p) & test == "", "x2", test)) %>%
group_by(test) %>%
rstatix::adjust_pvalue(p.col = "p", method = "BH")
# Read in csv to then append adjusted pvalues
write.csv(tab1_2_padjust, paste0("./Results/20220711_Results/Table_1_padjust_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
Table 2
# table2 variables
tabletwo_vars <- c("InvSimpson", "lithocholic acid", "deoxycholic acid",
"isodeoxycholic acid", "cholic acid", "glycocholic acid",
"3-oxolithocholic acid", "alloisolithocholic acid", "taurocholic acid",
"butyrate", "propionate", "acetate", "succinate", "desaminotyrosine",
"proteo_doms", "entero_doms")
# Create table 2
tab2_1 <- CreateTableOne(vars = tabletwo_vars, testNonNormal = "wilcox.test",
argsNonNormal = list(paired = FALSE, correct = TRUE, method = "BH"),
includeNA = FALSE, factorVars = tableone_cats, strata = "vital_status",
data = covid_lookup %>%
left_join(metab_quant %>%
pivot_wider(c(patient_ID, vital_status), names_from = "compound",
values_from = "mvalue")))
tab2_2 <- print(tab2_1, nonnormal = TRUE, formatOptions = list(big.mark = ","))
## Stratified by vital_status
## 0
## n 39
## InvSimpson (median [IQR]) 10.77 [7.53, 18.05]
## lithocholic acid (median [IQR]) 61.61 [4.30, 252.61]
## deoxycholic acid (median [IQR]) 74.09 [18.61, 246.34]
## isodeoxycholic acid (median [IQR]) 7.60 [0.70, 25.09]
## cholic acid (median [IQR]) 16.21 [1.48, 215.59]
## glycocholic acid (median [IQR]) 0.43 [0.18, 2.83]
## 3-oxolithocholic acid (median [IQR]) 4.71 [0.64, 54.90]
## alloisolithocholic acid (median [IQR]) 0.44 [0.00, 2.79]
## taurocholic acid (median [IQR]) 0.55 [0.22, 2.23]
## butyrate (median [IQR]) 0.51 [0.20, 2.39]
## propionate (median [IQR]) 1.55 [0.47, 4.94]
## acetate (median [IQR]) 3.87 [2.29, 20.44]
## succinate (median [IQR]) 0.59 [0.26, 1.58]
## desaminotyrosine (median [IQR]) 28.10 [22.74, 46.94]
## proteo_doms = 2 (%) 9 (23.1)
## entero_doms = 2 (%) 3 ( 7.7)
## Stratified by vital_status
## 1 p test
## n 32
## InvSimpson (median [IQR]) 10.80 [5.74, 20.70] 0.931 nonnorm
## lithocholic acid (median [IQR]) 9.76 [0.24, 60.07] 0.008 nonnorm
## deoxycholic acid (median [IQR]) 10.68 [0.49, 62.50] 0.003 nonnorm
## isodeoxycholic acid (median [IQR]) 2.89 [0.06, 10.93] 0.038 nonnorm
## cholic acid (median [IQR]) 7.92 [0.79, 88.15] 0.214 nonnorm
## glycocholic acid (median [IQR]) 0.45 [0.03, 11.78] 0.511 nonnorm
## 3-oxolithocholic acid (median [IQR]) 1.88 [0.06, 14.03] 0.086 nonnorm
## alloisolithocholic acid (median [IQR]) 0.20 [0.00, 6.18] 0.482 nonnorm
## taurocholic acid (median [IQR]) 0.30 [0.08, 24.06] 0.391 nonnorm
## butyrate (median [IQR]) 0.29 [0.06, 0.97] 0.286 nonnorm
## propionate (median [IQR]) 0.75 [0.19, 1.62] 0.169 nonnorm
## acetate (median [IQR]) 3.13 [1.17, 11.23] 0.175 nonnorm
## succinate (median [IQR]) 0.50 [0.18, 1.38] 0.431 nonnorm
## desaminotyrosine (median [IQR]) 22.63 [21.00, 32.30] 0.028 nonnorm
## proteo_doms = 2 (%) 16 (50.0) 0.035
## entero_doms = 2 (%) 3 ( 9.4) 1.000
# kableExtra::kable(tab2_2) %>% kableExtra::kable_classic()
# %>% kableExtra::save_kable(file = 'table_2_image.png',
# zoom = 5)
write.csv(tab2_2, paste0("./Results/20220711_Results/Table_2_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
# Need to adjust pvalues
tab2_2_padjust <- read.csv(paste0("./Results/20220711_Results/Table_2_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"))
tab2_2_padjust <- tab2_2_padjust %>%
mutate(test = ifelse(!is.na(p) & test == "", "x2", test)) %>%
group_by(test) %>%
rstatix::adjust_pvalue(p.col = "p", method = "BH")
# Read in csv to then append adjusted pvalues
write.csv(tab2_2_padjust, paste0("./Results/20220711_Results/Table_2_padjust_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
Table 3
cutpoints_unnest %>%
filter(subgroup %in% c("deoxycholic acid", "isodeoxycholic acid",
"lithocholic acid", "desaminotyrosine")) %>%
select(compound = subgroup, optimal_cutpoint) %>%
mutate(optimal_cutpoint = case_when(compound == "deoxycholic acid" ~
(optimal_cutpoint/deoxycholic_mw) * 1000, compound ==
"isodeoxycholic acid" ~ (optimal_cutpoint/isodeoxycholic_mw) *
1000, compound == "lithocholic acid" ~ (optimal_cutpoint/lithocholic_mw) *
1000, TRUE ~ optimal_cutpoint), optimal_cutpoint = round(optimal_cutpoint,
2), compound = str_to_title(compound), `0` = paste(">=",
optimal_cutpoint, "µM"), `1` = paste("<", optimal_cutpoint,
"µM")) %>%
select(compound, `0`, `1`) %>%
mutate(compound = as.factor(compound), compound = factor(compound,
levels = c("Deoxycholic Acid", "Isodeoxycholic Acid",
"Lithocholic Acid", "Desaminotyrosine"))) %>%
arrange(compound) %>%
write.csv(., paste0("./Results/20220711_Results/Table_3_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = F)
Table 4
# Variables labels
covid_lookup3 <- covid_lookup2 %>%
dplyr::rename(Age = age, `SOFA Score` = sofa, `Steroid Treatment` = steroid,
`Charlson Comorbidity Index` = Charlson, `Admission Location` = Admission,
`Proteobacteria Domination` = proteo_doms, `Glascow Coma Scale` = gcsmin,
`Microbiome Metabolic Profile` = mmp_score, `Chronic Kidney Disease` = CKD,
`Vancomycin Adminstration` = Vancomycin, `Neutrophil Percent` = Neutrophil,
`Bsophil Percent` = Basophil, `Eosinophil Percent` = Eosinophil) %>%
mutate(vital_cox = ifelse(vital_status == 1, 2, 1))
# Cox Model with Univariable inclusion of p<0.3
coxauc <- coxph(Surv(covid_lookup3$SurvTime, covid_lookup3$vital_cox) ~
Age + `Chronic Kidney Disease` + `SOFA Score` + `Vancomycin Adminstration` +
`Admission Location` + `Microbiome Metabolic Profile`,
data = covid_lookup3) %>%
tbl_regression(exp = TRUE)
gtsave(as_gt(coxauc), file = paste0("./Results/20220711_Results/Table_4_",
format(Sys.Date(), format = "%Y%m%d"), ".png"))
Table 5
covid_transition2 <- covid_transition %>%
left_join(covid_lookup2 %>%
select(patient_ID, Symptom_Days, mmp_score, grouped_mmp_score)) %>%
dplyr::rename(Age = age, `SOFA Score` = sofa, `Steroid Treatment` = steroid,
`Charlson Comorbidity Index` = Charlson, `Admission Location` = Admission,
`Proteobacteria Domination` = proteo_doms, BMI = bmi,
`Enterococcus Domination` = entero_doms, `Bacteriodetes Relative Abundance` = bacteroides_pctseqs,
Platelets = plt, Bilirubin = biliru, `Glascow Coma Scale` = gcsmin,
Creatinine = ap2_creatinine, Temp = ap2_temp_c, `Mean Arterial Pressure` = ap2_map,
`White Blood Cells` = ap2_wbc, Hematocrit = ap2_hematocrit,
`Chronic Kidney Disease` = CKD, `Coronary Artery Disease` = CAD,
Hyperlipidemia = HLD, `Microbiome Metabolic Profile` = mmp_score,
`Vasoactive Agents` = vasopresser) %>%
mutate(t_code = ifelse(Transition == "HFNC Failure", 1, 0))
log_t5_1 <- glm(t_code ~ Age + `Chronic Kidney Disease` + `Steroid Treatment` +
`SOFA Score` + Doxycycline + `Microbiome Metabolic Profile`,
data = covid_transition2) %>%
tbl_regression(exp = TRUE)
gtsave(as_gt(log_t5_1), file = paste0("./Results/20220711_Results/Table_5_",
format(Sys.Date(), format = "%Y%m%d"), ".png"))
Supplemental Tables
Supplemental Table 1
# Supplementary table one vars
s1_vars <- c("race", "sex", "age", "bmi", "HTN", "HLD", "DM",
"Malignancy", "CKD", "ESRD", "sofa", "Charlson", "APACHE",
"Symptom_Days", "Admission", "steroid", "remd_treat", "Betalactams",
"Levofloxicin", "Vancomycin", "Metronidazole", "Macrolides",
"Doxycycline", "Trimethoprim-Sulfamethoxazole")
# Transition table
tab_s1_1 <- CreateTableOne(vars = s1_vars, factorVars = tableone_cats,
strata = "Transition", data = covid_transition %>%
filter(Transition %in% c("HFNC Success", "HFNC Failure")) %>%
left_join(metab_quant %>%
pivot_wider(c(patient_ID, vital_status), names_from = "compound",
values_from = "mvalue")))
tab_s1_2 <- print(tab_s1_1, nonnormal = TRUE, formatOptions = list(big.mark = ","))
## Stratified by Transition
## HFNC Failure
## n 20
## race (%)
## Asian/Mideast Indian 0 ( 0.0)
## Black/African-American 13 (65.0)
## Hispanic 3 (15.0)
## More than one Race 0 ( 0.0)
## Native Hawaiian/Other Pacific Islander 0 ( 0.0)
## White 4 (20.0)
## sex = Male (%) 12 (60.0)
## age (median [IQR]) 67.96 [59.28, 76.10]
## bmi (median [IQR]) 30.73 [26.95, 33.77]
## HTN = 1 (%) 12 (60.0)
## HLD = 1 (%) 9 (45.0)
## DM = 1 (%) 9 (45.0)
## Malignancy = 1 (%) 3 (15.0)
## CKD = 1 (%) 4 (20.0)
## ESRD = 1 (%) 1 ( 5.0)
## sofa (median [IQR]) 9.00 [8.75, 9.00]
## Charlson (median [IQR]) 4.00 [2.00, 6.00]
## APACHE (median [IQR]) 21.00 [16.75, 29.50]
## Symptom_Days (median [IQR]) 5.00 [2.00, 8.00]
## Admission (%)
## ED 12 (60.0)
## Hospital Medicine 7 (35.0)
## OSH 1 ( 5.0)
## steroid = 1 (%) 18 (90.0)
## remd_treat = 1 (%) 18 (90.0)
## Betalactams = 1 (%) 4 (20.0)
## Levofloxicin = 1 (%) 0 ( 0.0)
## Vancomycin = 2 (%) 6 (30.0)
## Metronidazole = 1 (%) 1 ( 5.0)
## Macrolides = 1 (%) 7 (35.0)
## Doxycycline = 1 (%) 0 ( 0.0)
## Trimethoprim-Sulfamethoxazole = 1 (%) 2 (10.0)
## Stratified by Transition
## HFNC Success p test
## n 30
## race (%) 0.543
## Asian/Mideast Indian 1 ( 3.3)
## Black/African-American 20 (66.7)
## Hispanic 1 ( 3.3)
## More than one Race 1 ( 3.3)
## Native Hawaiian/Other Pacific Islander 1 ( 3.3)
## White 6 (20.0)
## sex = Male (%) 15 (50.0) 0.685
## age (median [IQR]) 59.47 [54.31, 67.43] 0.113 nonnorm
## bmi (median [IQR]) 31.05 [27.30, 38.80] 0.759 nonnorm
## HTN = 1 (%) 20 (66.7) 0.857
## HLD = 1 (%) 8 (26.7) 0.300
## DM = 1 (%) 11 (36.7) 0.768
## Malignancy = 1 (%) 6 (20.0) 0.940
## CKD = 1 (%) 1 ( 3.3) 0.149
## ESRD = 1 (%) 0 ( 0.0) 0.837
## sofa (median [IQR]) 4.00 [4.00, 5.75] <0.001 nonnorm
## Charlson (median [IQR]) 3.00 [2.00, 4.75] 0.361 nonnorm
## APACHE (median [IQR]) 18.00 [13.00, 21.25] 0.033 nonnorm
## Symptom_Days (median [IQR]) 5.00 [3.00, 6.75] 0.905 nonnorm
## Admission (%) 0.495
## ED 22 (73.3)
## Hospital Medicine 6 (20.0)
## OSH 2 ( 6.7)
## steroid = 1 (%) 22 (73.3) 0.279
## remd_treat = 1 (%) 24 (80.0) 0.581
## Betalactams = 1 (%) 5 (16.7) 1.000
## Levofloxicin = 1 (%) 1 ( 3.3) 1.000
## Vancomycin = 2 (%) 6 (20.0) 0.636
## Metronidazole = 1 (%) 1 ( 3.3) 1.000
## Macrolides = 1 (%) 7 (23.3) 0.563
## Doxycycline = 1 (%) 4 (13.3) 0.242
## Trimethoprim-Sulfamethoxazole = 1 (%) 4 (13.3) 1.000
write.csv(tab_s1_2, paste0("./Results/20220711_Results/Table_S1_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
# Need to adjust pvalues
tab_s1_2_padjust <- read.csv(paste0("./Results/20220711_Results/Table_S1_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"))
tab_s1_2_padjust <- tab_s1_2_padjust %>%
mutate(test = ifelse(!is.na(p) & test == "", "x2", test)) %>%
group_by(test) %>%
rstatix::adjust_pvalue(p.col = "p", method = "BH")
# Read in csv to then append adjusted pvalues
write.csv(tab_s1_2_padjust, paste0("./Results/20220711_Results/Table_5_padjust_",
format(Sys.Date(), format = "%Y%m%d"), ".csv"), row.names = TRUE)
Supplemental Figures
Supplemental Figure 3: Qualitative Metabolomic Heatmap
#### Heatmap START #### Build heatmap compound list
heatmap_cmpds <- metab_qual_raw %>%
ungroup() %>%
filter(compound %!in% c("tauro-alpha-muricholic acid-or-tauro-beta-muricholic acid",
"tauro-alpha-or-tauro-beta-muricholic acid", "ursocholic acid",
"7-12-dioxolithocholic acid", "toluate")) %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate",
compound)) %>%
distinct(compound) %>%
drop_na() %>%
mutate(compound = str_to_title(compound))
heatmap_data <- metab_qual_raw %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate",
compound), compound = str_to_title(compound)) %>%
filter(compound %in% heatmap_cmpds$compound) %>%
group_by(compound) %>%
mutate(median_val = median(mvalue, na.rm = T), heatmap_val = ifelse(log(mvalue/median_val,
base = 2) == -Inf, 0, log(mvalue/median_val, base = 2))) %>%
ungroup() %>%
select(-c(mvalue, median_val)) %>%
group_by(patient_ID, compound) %>%
slice_max(heatmap_val, with_ties = F, n = 1) %>%
ungroup() %>%
mutate(vital_status = ifelse(vital_status == 1, "Deceased",
"Alive"), vital_status = factor(vital_status, levels = c("Deceased",
"Alive")))
heatmap_labels <- heatmap_data %>%
mutate(compound = str_to_title(compound), class = case_when(grepl(x = compound,
pattern = "acid$", ignore.case = T) ~ "Bile Acid", compound %in%
c("Threonine", "Glycine", "Tyrosine", "Tyramine", "Serine",
"Leucine", "Isoleucine", "Valine", "Phenylalanine",
"Alanine", "Proline", "Aspartate", "Methionine",
"Glutamate", "Lysine", "Cysteine", "Tryptophan") ~
"Amino Acid", TRUE ~ "Short-Chain Fatty Acid")) %>%
arrange(class, compound) %>%
pivot_wider(c(vital_status, patient_ID), names_from = compound,
values_from = heatmap_val) %>%
group_by(patient_ID) %>%
arrange(patient_ID) %>%
ungroup() %>%
select(vital_status) %>%
mutate(vital_status = factor(vital_status, levels = c("Deceased",
"Alive")))
# Build heatmap compound order (row order) and compound
# class (row slice)
heatmap_order <- metab_qual_raw %>%
ungroup() %>%
filter(compound %!in% c("tauro-alpha-muricholic acid-or-tauro-beta-muricholic acid",
"tauro-alpha-or-tauro-beta-muricholic acid", "ursocholic acid",
"7-12-dioxolithocholic acid", "toluate")) %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate",
compound)) %>%
distinct(compound) %>%
drop_na() %>%
mutate(compound = str_to_title(compound), class = case_when(grepl(x = compound,
pattern = "acid$", ignore.case = T) ~ "Bile Acid", compound %in%
c("Threonine", "Glycine", "Tyrosine", "Tyramine", "Serine",
"Leucine", "Isoleucine", "Valine", "Phenylalanine",
"Alanine", "Proline", "Aspartate", "Methionine",
"Glutamate", "Lysine", "Cysteine", "Tryptophan") ~
"Amino Acid", TRUE ~ "Fatty Acid")) %>%
arrange(class, compound)
# Heatmap legend color
col_fun <- colorRamp2(breaks = c(-10, 0, 10), colors = c("#00aaad",
"white", "#ad003a"))
gg_covid_heatmap <- heatmap_data %>%
mutate(compound = str_to_title(compound), class = case_when(grepl(x = compound,
pattern = "acid$", ignore.case = T) ~ "Bile Acid", compound %in%
c("Threonine", "Glycine", "Tyrosine", "Tyramine", "Serine",
"Leucine", "Isoleucine", "Valine", "Phenylalanine",
"Alanine", "Proline", "Aspartate", "Methionine",
"Glutamate", "Lysine", "Cysteine", "Tryptophan") ~
"Amino Acid", TRUE ~ "Fatty Acid")) %>%
arrange(class, compound) %>%
pivot_wider(c(vital_status, patient_ID), names_from = compound,
values_from = heatmap_val) %>%
group_by(patient_ID) %>%
arrange(patient_ID) %>%
ungroup() %>%
select(-vital_status) %>%
column_to_rownames("patient_ID") %>%
as.matrix() %>%
t() %>%
Heatmap(name = "Fold Change (log2)", col = col_fun, rect_gp = gpar(col = "black",
lwd = 0.2), column_names_gp = grid::gpar(fontsize = 8),
column_gap = unit(6, "mm"), column_split = heatmap_labels,
column_title_gp = gpar(fontsize = 14), column_title_rot = 0,
cluster_columns = TRUE, show_column_names = FALSE, show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 8), row_gap = unit(3.5,
"mm"), row_names_side = c("left"), row_order = heatmap_order$compound,
row_split = heatmap_order$class, cluster_rows = TRUE,
show_row_dend = FALSE, row_dend_width = unit(4, "in"),
heatmap_width = unit(12, "in"))
gg_covid_heatmap
pdf(paste0("./Results/20220711_Results/Figure_S3_", format(Sys.Date(),
format = "%Y%m%d"), ".pdf"), height = 12, width = 17, onefile = F)
gg_covid_heatmap
dev.off()
## quartz_off_screen
## 2
#### Heatmap END ####
Save Image
save.image(file = paste0("SARS-CoV-2_Modeling_", format(Sys.Date(),
format = "%Y%m%d"), ".RData"))